Blood Test API Endpoints

Production endpoint for blood test analysis. Upload one or more blood test images or a PDF and receive structured parameters, patient and laboratory metadata, and a full clinical interpretation in any of the 100 supported languages. Consumes 1 credit per request.

Request Parameters

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v12/18-09-2026/analyze" \
  -F "username=YOUR_USERNAME" \
  -F "password=YOUR_PASSWORD" \
  -F "language=en" \
  -F "file=@blood_test.pdf"

Python Example

import requests

def analyze_blood_test(file_paths, username, password, language="en"):
    """
    Analyze a blood test with Kantesti Blood Test Analysis v12.

    Args:
        file_paths: One or more paths to blood test images, or a single PDF
        username: API username
        password: API password
        language: Report language code (default: en)

    Returns:
        dict: Structured parameters, metadata and clinical interpretation
    """
    url = "https://app.aibloodtestinterpret.com/api/v12/18-09-2026/analyze"

    handles = [open(path, "rb") for path in file_paths]
    try:
        files = [("file", (path, handle)) for path, handle in zip(file_paths, handles)]
        data = {"username": username, "password": password, "language": language}
        response = requests.post(url, files=files, data=data, timeout=300)
        response.raise_for_status()
        return response.json()
    finally:
        for handle in handles:
            handle.close()

# Example usage
if __name__ == "__main__":
    result = analyze_blood_test(
        file_paths=["blood_test.pdf"],
        username="your_username",
        password="your_password",
        language="en"
    )
    print(f"Status: {result['status']}")
    for param in result["data"]["parameters"]:
        print(f"  {param['short_name']}: {param['result']} {param['unit']} ({param['evaluation']})")

Example Response

{
  "status": "success",
  "api_version": "v12",
  "data": {
    "metadata": {
      "patient_name": "Jan Novak",
      "patient_age": "45",
      "patient_sex": "Male",
      "lab_name": "BioLAB Medical Center",
      "lab_city": "Prague",
      "lab_country": "Czech Republic",
      "lab_date": "2026-09-11",
      "results_date": "2026-09-12"
    },
    "parameters": [
      {"short_name": "Glucose", "long_name": "Fasting Blood Glucose", "result": "92", "unit": "mg/dL", "reference_range": "74 - 100", "range_normal_min": 74, "range_normal_max": 100, "type": "range", "evaluation": "normal"},
      {"short_name": "ALT", "long_name": "Alanine aminotransferase", "result": "65", "unit": "U/L", "reference_range": "< 45", "range_normal_min": 7, "range_normal_max": 45, "type": "range", "evaluation": "high"},
      {"short_name": "Creatinine", "long_name": "Creatinine", "result": "0.9", "unit": "mg/dL", "reference_range": "0.7 - 1.2", "range_normal_min": 0.7, "range_normal_max": 1.2, "type": "range", "evaluation": "normal"}
    ],
    "interpretation": [
      {"shortcode": "overview", "item": "Most parameters are within their reference ranges."},
      {"shortcode": "key_findings", "item": "Alanine aminotransferase is above the reference range, which warrants a follow-up liver panel."}
    ]
  },
  "timestamp": "2026-09-18T10:30:00Z"
}

Production endpoint for blood test analysis. Consumes 1 credit per request.

Request Parameters

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v11/01-06-2025/analyze" \
  -F "username=YOUR_USERNAME" \
  -F "password=YOUR_PASSWORD" \
  -F "language=en" \
  -F "file=@blood_test.pdf"

Python Example

import requests

def analyze_blood_test(file_path: str, username: str, password: str, language: str = "en"):
    """
    Analyze a blood test file using Kantesti API.

    Args:
        file_path: Path to the blood test PDF or image
        username: API username
        password: API password
        language: Response language code (default: en)

    Returns:
        dict: API response with analysis results
    """
    url = "https://app.aibloodtestinterpret.com/api/v11/01-06-2025/analyze"

    with open(file_path, "rb") as f:
        files = {"file": (file_path, f, "application/pdf")}
        data = {
            "username": username,
            "password": password,
            "language": language
        }

        response = requests.post(url, files=files, data=data, timeout=120)
        response.raise_for_status()
        return response.json()

# Example usage
if __name__ == "__main__":
    result = analyze_blood_test(
        file_path="blood_test.pdf",
        username="your_username",
        password="your_password",
        language="en"
    )
    print(f"Status: {result['status']}")
    print(f"Parameters found: {len(result['data']['parameters'])}")

C++ Example (libcurl)

#include <iostream>
#include <string>
#include <curl/curl.h>

size_t WriteCallback(void* contents, size_t size, size_t nmemb, std::string* userp) {
    userp->append((char*)contents, size * nmemb);
    return size * nmemb;
}

std::string analyzeBloodTest(const std::string& filePath,
                              const std::string& username,
                              const std::string& password,
                              const std::string& language = "en") {
    CURL* curl = curl_easy_init();
    std::string response;

    if (curl) {
        curl_mime* form = curl_mime_init(curl);
        curl_mimepart* field;

        // Add username
        field = curl_mime_addpart(form);
        curl_mime_name(field, "username");
        curl_mime_data(field, username.c_str(), CURL_ZERO_TERMINATED);

        // Add password
        field = curl_mime_addpart(form);
        curl_mime_name(field, "password");
        curl_mime_data(field, password.c_str(), CURL_ZERO_TERMINATED);

        // Add language
        field = curl_mime_addpart(form);
        curl_mime_name(field, "language");
        curl_mime_data(field, language.c_str(), CURL_ZERO_TERMINATED);

        // Add file
        field = curl_mime_addpart(form);
        curl_mime_name(field, "file");
        curl_mime_filedata(field, filePath.c_str());

        curl_easy_setopt(curl, CURLOPT_URL,
            "https://app.aibloodtestinterpret.com/api/v11/01-06-2025/analyze");
        curl_easy_setopt(curl, CURLOPT_MIMEPOST, form);
        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
        curl_easy_setopt(curl, CURLOPT_WRITEDATA, &response);
        curl_easy_setopt(curl, CURLOPT_TIMEOUT, 120L);

        CURLcode res = curl_easy_perform(curl);

        curl_mime_free(form);
        curl_easy_cleanup(curl);
    }
    return response;
}

int main() {
    std::string result = analyzeBloodTest("blood_test.pdf", "username", "password");
    std::cout << result << std::endl;
    return 0;
}

Postman Configuration

{
  "info": {
    "name": "Kantesti Blood Test API",
    "schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json"
  },
  "item": [
    {
      "name": "Analyze Blood Test",
      "request": {
        "method": "POST",
        "header": [],
        "body": {
          "mode": "formdata",
          "formdata": [
            {"key": "username", "value": "{{api_username}}", "type": "text"},
            {"key": "password", "value": "{{api_password}}", "type": "text"},
            {"key": "language", "value": "en", "type": "text"},
            {"key": "file", "type": "file", "src": "/path/to/blood_test.pdf"}
          ]
        },
        "url": {
          "raw": "https://app.aibloodtestinterpret.com/api/v11/01-06-2025/analyze",
          "protocol": "https",
          "host": ["app", "aibloodtestinterpret", "com"],
          "path": ["api", "v11", "01-06-2025", "analyze"]
        }
      }
    }
  ]
}

Example Response

{
  "status": "success",
  "data": {
    "metadata": {
      "patient_name": "Jan Novák",
      "lab_name": "BioLAB Medical Center",
      "lab_city": "Prague",
      "lab_country": "Czech Republic",
      "lab_date": "2025-05-11",
      "patient_age": "45",
      "patient_sex": "Male"
    },
    "parameters": [
      {
        "short_name": "WBC",
        "long_name": "White Blood Cell Count",
        "category": "Complete Blood Count",
        "result": 7.2,
        "unit": "10^9/L",
        "evaluation": "normal",
        "range_min": 2.0,
        "range_max": 12.0,
        "range_normal_min": 4.0,
        "range_normal_max": 10.0,
        "short_description": "Measures the total white blood cells in blood.",
        "long_description": "White blood cells (leukocytes) are essential for immune function..."
      },
      {
        "short_name": "HGB",
        "long_name": "Hemoglobin",
        "category": "Complete Blood Count",
        "result": 14.5,
        "unit": "g/dL",
        "evaluation": "normal",
        "range_min": 10.0,
        "range_max": 18.0,
        "range_normal_min": 13.5,
        "range_normal_max": 17.5,
        "short_description": "Protein in red blood cells that carries oxygen.",
        "long_description": "Hemoglobin is the iron-containing protein responsible for oxygen transport..."
      }
    ],
    "interpretation": [
      {
        "title": "Overall Health Assessment",
        "shortcode": "overall_health_assessment",
        "subsections": [
          {
            "subtitle": "Comprehensive Overview",
            "shortcode": "overall_health_assessment_overview",
            "items": [
              {"item": "The patient demonstrates preserved overall organ function with normal hematological parameters."},
              {"item": "No significant abnormalities detected in the complete blood count."}
            ]
          }
        ]
      }
    ]
  },
  "api_version": "v11",
  "timestamp": "2025-12-22T10:30:00Z"
}

Response Fields Reference

Root Level

data.metadata Object

data.parameters Array Item

data.interpretation Array Item

Complete Response Example

{
  "status": "success",
  "data": {
    "metadata": {
      "patient_name": "Anna Müller",
      "lab_name": "MedLab Diagnostics International",
      "lab_city": "Munich",
      "lab_country": "Germany",
      "lab_date": "2025-12-15",
      "results_date": "2025-12-16",
      "patient_age": "38",
      "patient_sex": "female"
    },
    "parameters": [
      {
        "short_name": "WBC",
        "long_name": "White Blood Cell Count",
        "category": "Complete Blood Count",
        "result": "6.8",
        "unit": "10^9/L",
        "evaluation": "normal",
        "range_min": "4.0",
        "range_max": "11.0",
        "range_normal_min": "4.5",
        "range_normal_max": "10.0",
        "short_description": "Measures the total number of white blood cells.",
        "long_description": "White blood cells (leukocytes) are essential components of the immune system..."
      },
      {
        "short_name": "RBC",
        "long_name": "Red Blood Cell Count",
        "category": "Complete Blood Count",
        "result": "4.52",
        "unit": "10^12/L",
        "evaluation": "normal",
        "range_min": "3.8",
        "range_max": "5.8",
        "range_normal_min": "4.0",
        "range_normal_max": "5.5",
        "short_description": "Measures the total number of red blood cells.",
        "long_description": "Red blood cells (erythrocytes) carry oxygen from the lungs to body tissues..."
      },
      {
        "short_name": "HGB",
        "long_name": "Hemoglobin",
        "category": "Complete Blood Count",
        "result": "13.2",
        "unit": "g/dL",
        "evaluation": "normal",
        "range_min": "11.5",
        "range_max": "16.0",
        "range_normal_min": "12.0",
        "range_normal_max": "15.5",
        "short_description": "Protein in red blood cells that carries oxygen.",
        "long_description": "Hemoglobin is the iron-containing protein in red blood cells responsible for oxygen transport..."
      },
      {
        "short_name": "HCT",
        "long_name": "Hematocrit",
        "category": "Complete Blood Count",
        "result": "39.8",
        "unit": "%",
        "evaluation": "normal",
        "range_min": "35.0",
        "range_max": "47.0",
        "range_normal_min": "36.0",
        "range_normal_max": "44.0",
        "short_description": "Percentage of blood volume occupied by red blood cells.",
        "long_description": "Hematocrit measures the proportion of blood that consists of red blood cells..."
      },
      {
        "short_name": "PLT",
        "long_name": "Platelet Count",
        "category": "Complete Blood Count",
        "result": "245",
        "unit": "10^9/L",
        "evaluation": "normal",
        "range_min": "150",
        "range_max": "400",
        "range_normal_min": "150",
        "range_normal_max": "350",
        "short_description": "Measures the number of platelets in blood.",
        "long_description": "Platelets (thrombocytes) are essential for blood clotting and wound healing..."
      },
      {
        "short_name": "GLU",
        "long_name": "Fasting Glucose",
        "category": "Metabolic Panel",
        "result": "102",
        "unit": "mg/dL",
        "evaluation": "borderline_high",
        "range_min": "70",
        "range_max": "140",
        "range_normal_min": "70",
        "range_normal_max": "99",
        "short_description": "Measures blood sugar level after fasting.",
        "long_description": "Fasting glucose is a key indicator of how well the body metabolizes sugar..."
      },
      {
        "short_name": "TC",
        "long_name": "Total Cholesterol",
        "category": "Lipid Panel",
        "result": "218",
        "unit": "mg/dL",
        "evaluation": "borderline_high",
        "range_min": "0",
        "range_max": "300",
        "range_normal_min": "0",
        "range_normal_max": "200",
        "short_description": "Measures total cholesterol in blood.",
        "long_description": "Total cholesterol is the sum of HDL, LDL, and VLDL cholesterol levels..."
      },
      {
        "short_name": "HDL",
        "long_name": "HDL Cholesterol",
        "category": "Lipid Panel",
        "result": "58",
        "unit": "mg/dL",
        "evaluation": "normal",
        "range_min": "35",
        "range_max": "100",
        "range_normal_min": "50",
        "range_normal_max": "90",
        "short_description": "Measures 'good' cholesterol level.",
        "long_description": "HDL (high-density lipoprotein) cholesterol helps remove other forms of cholesterol..."
      },
      {
        "short_name": "LDL",
        "long_name": "LDL Cholesterol",
        "category": "Lipid Panel",
        "result": "142",
        "unit": "mg/dL",
        "evaluation": "high",
        "range_min": "0",
        "range_max": "200",
        "range_normal_min": "0",
        "range_normal_max": "100",
        "short_description": "Measures 'bad' cholesterol level.",
        "long_description": "LDL (low-density lipoprotein) cholesterol can build up in artery walls..."
      },
      {
        "short_name": "TRIG",
        "long_name": "Triglycerides",
        "category": "Lipid Panel",
        "result": "156",
        "unit": "mg/dL",
        "evaluation": "borderline_high",
        "range_min": "0",
        "range_max": "500",
        "range_normal_min": "0",
        "range_normal_max": "150",
        "short_description": "Measures fat in the blood.",
        "long_description": "Triglycerides are a type of fat found in the blood that stores excess energy..."
      }
    ],
    "interpretation": [
      {
        "title": "Overall Health Assessment",
        "shortcode": "overall_health_assessment",
        "subsections": [
          {
            "subtitle": "Comprehensive Overview",
            "shortcode": "overall_health_assessment_overview",
            "items": [
              {"item": "The patient demonstrates generally healthy hematological parameters with all blood count values within normal ranges."},
              {"item": "Complete blood count results indicate adequate oxygen-carrying capacity and immune function."},
              {"item": "Lipid panel shows areas requiring attention, particularly LDL cholesterol levels."}
            ]
          }
        ]
      },
      {
        "title": "Metabolic Analysis",
        "shortcode": "metabolic_analysis",
        "subsections": [
          {
            "subtitle": "Blood Sugar Assessment",
            "shortcode": "metabolic_analysis_glucose",
            "items": [
              {"item": "Fasting glucose at 102 mg/dL is slightly elevated, indicating pre-diabetic range."},
              {"item": "Recommend lifestyle modifications including regular exercise and reduced simple carbohydrate intake."},
              {"item": "Follow-up HbA1c testing recommended in 3 months to assess long-term glucose control."}
            ]
          }
        ]
      },
      {
        "title": "Cardiovascular Risk Assessment",
        "shortcode": "cardiovascular_risk",
        "subsections": [
          {
            "subtitle": "Lipid Profile Analysis",
            "shortcode": "cardiovascular_risk_lipids",
            "items": [
              {"item": "LDL cholesterol at 142 mg/dL exceeds optimal levels and warrants intervention."},
              {"item": "HDL cholesterol at 58 mg/dL provides moderate cardiovascular protection."},
              {"item": "Triglycerides slightly elevated; consider dietary modifications to reduce saturated fat intake."},
              {"item": "Total cholesterol to HDL ratio of 3.76 indicates moderate cardiovascular risk."}
            ]
          }
        ]
      },
      {
        "title": "Recommendations",
        "shortcode": "recommendations",
        "subsections": [
          {
            "subtitle": "Lifestyle Modifications",
            "shortcode": "recommendations_lifestyle",
            "items": [
              {"item": "Increase aerobic physical activity to at least 150 minutes per week."},
              {"item": "Adopt a Mediterranean-style diet rich in vegetables, fruits, and healthy fats."},
              {"item": "Limit processed foods, saturated fats, and added sugars."},
              {"item": "Consider consultation with a nutritionist for personalized dietary guidance."}
            ]
          },
          {
            "subtitle": "Follow-up Testing",
            "shortcode": "recommendations_followup",
            "items": [
              {"item": "Repeat lipid panel in 3 months after implementing lifestyle changes."},
              {"item": "HbA1c test recommended to assess average blood glucose levels."},
              {"item": "Annual comprehensive metabolic panel for ongoing monitoring."}
            ]
          }
        ]
      }
    ]
  },
  "api_version": "v11",
  "timestamp": "2025-12-16T14:32:18Z"
}

Production endpoint with comprehensive health score calculation and disease risk analysis. Takes the same request as /api/v12/18-09-2026/analyze and adds the fields below to the response. Consumes 1 credit per request.

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v12/health-score/analyze" \
  -F "username=YOUR_USERNAME" \
  -F "password=YOUR_PASSWORD" \
  -F "language=en" \
  -F "file=@blood_test.pdf"

Additional Response Fields

{
  "health_score": {
    "overall": 78,
    "optimal": 4,
    "normal": 12,
    "warning": 3,
    "critical": 1,
    "total_parameters": 20,
    "score_interpretation": "good",
    "recommendations": [
      "Consider increasing vitamin D intake",
      "Schedule follow-up for cholesterol levels"
    ]
  },
  "disease_risks": [
    {"name": "Cardiovascular Disease", "percentage": "18%", "severity": "low"},
    {"name": "Type 2 Diabetes", "percentage": "12%", "severity": "low"},
    {"name": "Metabolic Syndrome", "percentage": "25%", "severity": "moderate"}
  ]
}

Production endpoint with comprehensive health score calculation and disease risk analysis.

Additional Response Fields

{
  "health_score": {
    "overall": 78,
    "optimal": 4,
    "normal": 12,
    "warning": 3,
    "critical": 1,
    "total_parameters": 20,
    "score_interpretation": "good",
    "recommendations": [
      "Consider increasing vitamin D intake",
      "Schedule follow-up for cholesterol levels"
    ]
  },
  "disease_risks": [
    {"name": "Cardiovascular Disease", "percentage": "18%", "severity": "low"},
    {"name": "Type 2 Diabetes", "percentage": "12%", "severity": "low"},
    {"name": "Metabolic Syndrome", "percentage": "25%", "severity": "moderate"}
  ]
}

Analyzes blood test parameter trends over multiple test dates to identify patterns and provide health insights.

Request Parameters

analysis_options Object

blood_tests Array Structure

*Either lab_date or results_date is required for each blood test.

blood_tests[].parameters Array Structure

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/analytics/trends/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "YOUR_USERNAME",
    "password": "YOUR_PASSWORD",
    "language": "en",
    "analysis_type": "comprehensive",
    "analysis_options": {
      "include_predictions": true,
      "include_statistics": true,
      "include_charts": true
    },
    "blood_tests": [
      {
        "lab_date": "2024-01-15",
        "parameters": [
          {"name": "Fasting Glucose", "short_name": "GLU", "value": 98, "unit": "mg/dL"},
          {"name": "Hemoglobin A1c", "short_name": "HBA1C", "value": 5.6, "unit": "%"},
          {"name": "Total Cholesterol", "short_name": "CHOL", "value": 185, "unit": "mg/dL"},
          {"name": "LDL Cholesterol", "short_name": "LDL", "value": 110, "unit": "mg/dL"},
          {"name": "HDL Cholesterol", "short_name": "HDL", "value": 52, "unit": "mg/dL"}
        ]
      },
      {
        "lab_date": "2024-05-20",
        "parameters": [
          {"name": "Fasting Glucose", "short_name": "GLU", "value": 92, "unit": "mg/dL"},
          {"name": "Hemoglobin A1c", "short_name": "HBA1C", "value": 5.4, "unit": "%"},
          {"name": "Total Cholesterol", "short_name": "CHOL", "value": 178, "unit": "mg/dL"},
          {"name": "LDL Cholesterol", "short_name": "LDL", "value": 102, "unit": "mg/dL"},
          {"name": "HDL Cholesterol", "short_name": "HDL", "value": 55, "unit": "mg/dL"}
        ]
      },
      {
        "lab_date": "2024-09-10",
        "parameters": [
          {"name": "Fasting Glucose", "short_name": "GLU", "value": 88, "unit": "mg/dL"},
          {"name": "Hemoglobin A1c", "short_name": "HBA1C", "value": 5.3, "unit": "%"},
          {"name": "Total Cholesterol", "short_name": "CHOL", "value": 168, "unit": "mg/dL"},
          {"name": "LDL Cholesterol", "short_name": "LDL", "value": 92, "unit": "mg/dL"},
          {"name": "HDL Cholesterol", "short_name": "HDL", "value": 58, "unit": "mg/dL"}
        ]
      },
      {
        "lab_date": "2024-12-18",
        "parameters": [
          {"name": "Fasting Glucose", "short_name": "GLU", "value": 85, "unit": "mg/dL"},
          {"name": "Hemoglobin A1c", "short_name": "HBA1C", "value": 5.2, "unit": "%"},
          {"name": "Total Cholesterol", "short_name": "CHOL", "value": 162, "unit": "mg/dL"},
          {"name": "LDL Cholesterol", "short_name": "LDL", "value": 85, "unit": "mg/dL"},
          {"name": "HDL Cholesterol", "short_name": "HDL", "value": 62, "unit": "mg/dL"}
        ]
      }
    ]
  }'

Python Example

import requests
from typing import List, Dict, Optional

def analyze_trends(
    username: str,
    password: str,
    blood_tests: List[Dict],
    language: str = "en",
    analysis_type: str = "comprehensive"
) -> Dict:
    """
    Analyze blood test trends over time.

    Args:
        username: API username
        password: API password
        blood_tests: List of blood test objects with lab_date and parameters
        language: Response language code
        analysis_type: comprehensive, quick, or focused

    Returns:
        dict: Trend analysis results
    """
    url = "https://app.aibloodtestinterpret.com/api/v1/analytics/trends/analyze"

    payload = {
        "username": username,
        "password": password,
        "language": language,
        "analysis_type": analysis_type,
        "blood_tests": blood_tests
    }

    response = requests.post(url, json=payload, timeout=120)
    response.raise_for_status()
    return response.json()

# Example usage
if __name__ == "__main__":
    blood_tests = [
        {
            "lab_date": "2025-01-15",
            "parameters": [
                {"short_name": "HGB", "result": 12.5, "unit": "g/dL"},
                {"short_name": "CHOL", "result": 220, "unit": "mg/dL"}
            ]
        },
        {
            "lab_date": "2025-06-15",
            "parameters": [
                {"short_name": "HGB", "result": 13.2, "unit": "g/dL"},
                {"short_name": "CHOL", "result": 195, "unit": "mg/dL"}
            ]
        },
        {
            "lab_date": "2025-12-01",
            "parameters": [
                {"short_name": "HGB", "result": 14.1, "unit": "g/dL"},
                {"short_name": "CHOL", "result": 180, "unit": "mg/dL"}
            ]
        }
    ]

    result = analyze_trends(
        username="your_username",
        password="your_password",
        blood_tests=blood_tests
    )

    print(f"Overall trend: {result['data']['overall_trend']}")
    for trend in result['data']['parameter_trends']:
        print(f"{trend['parameter']}: {trend['direction']} ({trend['change_percent']}%)")

C++ Example (libcurl + nlohmann/json)

#include <iostream>
#include <string>
#include <curl/curl.h>
#include <nlohmann/json.hpp>

using json = nlohmann::json;

size_t WriteCallback(void* contents, size_t size, size_t nmemb, std::string* userp) {
    userp->append((char*)contents, size * nmemb);
    return size * nmemb;
}

std::string analyzeTrends(const std::string& username,
                          const std::string& password,
                          const json& bloodTests,
                          const std::string& language = "en") {
    CURL* curl = curl_easy_init();
    std::string response;

    if (curl) {
        json payload = {
            {"username", username},
            {"password", password},
            {"language", language},
            {"analysis_type", "comprehensive"},
            {"blood_tests", bloodTests}
        };

        std::string jsonStr = payload.dump();

        struct curl_slist* headers = nullptr;
        headers = curl_slist_append(headers, "Content-Type: application/json");

        curl_easy_setopt(curl, CURLOPT_URL,
            "https://app.aibloodtestinterpret.com/api/v1/analytics/trends/analyze");
        curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);
        curl_easy_setopt(curl, CURLOPT_POSTFIELDS, jsonStr.c_str());
        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
        curl_easy_setopt(curl, CURLOPT_WRITEDATA, &response);
        curl_easy_setopt(curl, CURLOPT_TIMEOUT, 120L);

        CURLcode res = curl_easy_perform(curl);

        curl_slist_free_all(headers);
        curl_easy_cleanup(curl);
    }
    return response;
}

int main() {
    json bloodTests = json::array({
        {{"lab_date", "2025-01-15"}, {"parameters", {{{"short_name", "HGB"}, {"result", 12.5}, {"unit", "g/dL"}}}}},
        {{"lab_date", "2025-06-15"}, {"parameters", {{{"short_name", "HGB"}, {"result", 13.2}, {"unit", "g/dL"}}}}},
        {{"lab_date", "2025-12-01"}, {"parameters", {{{"short_name", "HGB"}, {"result", 14.1}, {"unit", "g/dL"}}}}}
    });

    std::string result = analyzeTrends("username", "password", bloodTests);
    std::cout << result << std::endl;
    return 0;
}

Response Fields Reference

parameter_trends Object Structure

Example Response

{
  "api_version": "1.0.0",
  "status": "success",
  "message": "Trend analysis completed successfully",
  "timestamp": "2025-12-22T01:12:49.262700Z",
  "data": {
    "analysis_id": "TRD-49B4C616",
    "analysis_period": {
      "start_date": "2024-01-15",
      "end_date": "2024-12-18",
      "span_months": 11,
      "total_tests": 4
    },
    "categories": [
      "Metabolic Panel",
      "Vitamins",
      "Diabetes Markers",
      "Iron Studies",
      "Lipid Panel",
      "Complete Blood Count"
    ],
    "chart_config": {
      "dates": ["Jan 2024", "May 2024", "Sep 2024", "Dec 2024"],
      "raw_dates": ["2024-01-15", "2024-05-20", "2024-09-10", "2024-12-18"]
    },
    "language": "en",
    "overall_health_trend": {
      "summary": "Overall, the blood test parameters show positive trends with improvements in hemoglobin, lipid profile including LDL and HDL cholesterol, vitamin D status, and iron stores.",
      "health_risks": [],
      "recommendations": []
    },
    "parameter_trends": [
      {
        "parameter": "Hemoglobin (Hb)",
        "short_name": "Hemoglobin",
        "category": "Complete Blood Count",
        "unit": "g/dL",
        "original_names": ["Hemoglobin", "HGB"],
        "trend_data": [
          {"date": "2024-01-15", "value": 12.8},
          {"date": "2024-05-20", "value": 13.5},
          {"date": "2024-09-10", "value": 14.2},
          {"date": "2024-12-18", "value": 14.8}
        ],
        "statistical_analysis": {
          "average": 13.82,
          "min": 12.8,
          "max": 14.8,
          "standard_deviation": 0.87,
          "trend_direction": "upward",
          "trend_strength": "moderate"
        },
        "analysis": {
          "description": "Hemoglobin measures the oxygen-carrying protein in red blood cells.",
          "significant_variations": "Initially low at 12.8 g/dL, then gradually increased to 14.8 g/dL.",
          "trend": "increasing",
          "unit": "g/dL"
        },
        "interpretation": "Hemoglobin measures the oxygen-carrying protein in red blood cells."
      },
      {
        "parameter": "Low-Density Lipoprotein Cholesterol (LDL-C)",
        "short_name": "Low-Densit",
        "category": "Lipid Panel",
        "unit": "mg/dL",
        "trend_data": [
          {"date": "2024-01-15", "value": 110.0},
          {"date": "2024-05-20", "value": 102.0},
          {"date": "2024-09-10", "value": 92.0},
          {"date": "2024-12-18", "value": 85.0}
        ],
        "statistical_analysis": {
          "average": 97.25,
          "min": 85.0,
          "max": 110.0,
          "standard_deviation": 11.0,
          "trend_direction": "downward",
          "trend_strength": "strong"
        },
        "analysis": {
          "description": "LDL-C is the 'bad' cholesterol associated with increased heart disease risk.",
          "significant_variations": "LDL-C shifted from high (110 mg/dL) to normal (85 mg/dL).",
          "trend": "decreasing"
        }
      }
    ],
    "risk_factors": [],
    "sandbox_mode": false
  }
}

Generates comprehensive nutrition and supplement recommendations based on blood test parameters and patient profile.

Request Parameters

Patient Object Schema

Patient demographics object with gender (see values) and activity_level (see values).

{
  "age": 45,
  "gender": "male",
  "weight": 82,
  "height": 178,
  "activity_level": "moderate",
  "conditions": ["hypertension"],
  "medications": ["lisinopril"],
  "allergies": ["shellfish"]
}

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/nutrition/diet-plan/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "YOUR_USERNAME",
    "password": "YOUR_PASSWORD",
    "language": "en",
    "patient": {
      "age": 45,
      "gender": "male",
      "weight": 82,
      "height": 178,
      "activity_level": "moderate",
      "conditions": ["hypertension"],
      "allergies": ["shellfish"]
    },
    "blood_test": {
      "lab_date": "2025-12-01",
      "parameters": [
        {"short_name": "VITD", "result": 18, "unit": "ng/mL"},
        {"short_name": "FER", "result": 25, "unit": "ng/mL"},
        {"short_name": "B12", "result": 280, "unit": "pg/mL"},
        {"short_name": "CHOL", "result": 210, "unit": "mg/dL"},
        {"short_name": "HDL", "result": 42, "unit": "mg/dL"},
        {"short_name": "LDL", "result": 140, "unit": "mg/dL"}
      ]
    },
    "health_goals": ["lower_cholesterol", "increase_energy"],
    "dietary_restrictions": ["low_sodium"]
  }'

Python Example

import requests
from typing import Dict, List, Optional

def get_nutrition_plan(
    username: str,
    password: str,
    patient: Dict,
    blood_test: Dict,
    health_goals: Optional[List[str]] = None,
    dietary_restrictions: Optional[List[str]] = None,
    language: str = "en"
) -> Dict:
    """
    Get personalized nutrition and supplement recommendations.

    Args:
        username: API username
        password: API password
        patient: Patient demographics and health info
        blood_test: Blood test parameters
        health_goals: Optional health goals
        dietary_restrictions: Optional dietary restrictions
        language: Response language

    Returns:
        dict: Nutrition plan with supplements
    """
    url = "https://app.aibloodtestinterpret.com/api/v1/nutrition/diet-plan/analyze"

    payload = {
        "username": username,
        "password": password,
        "language": language,
        "patient": patient,
        "blood_test": blood_test
    }

    if health_goals:
        payload["health_goals"] = health_goals
    if dietary_restrictions:
        payload["dietary_restrictions"] = dietary_restrictions

    response = requests.post(url, json=payload, timeout=120)
    response.raise_for_status()
    return response.json()

# Example usage
if __name__ == "__main__":
    patient = {
        "age": 45,
        "gender": "male",
        "weight": 82,
        "height": 178,
        "activity_level": "moderate",
        "conditions": ["hypertension"],
        "allergies": ["shellfish"]
    }

    blood_test = {
        "lab_date": "2025-12-01",
        "parameters": [
            {"short_name": "VITD", "result": 18, "unit": "ng/mL"},
            {"short_name": "FER", "result": 25, "unit": "ng/mL"},
            {"short_name": "CHOL", "result": 210, "unit": "mg/dL"}
        ]
    }

    result = get_nutrition_plan(
        username="your_username",
        password="your_password",
        patient=patient,
        blood_test=blood_test,
        health_goals=["lower_cholesterol", "increase_energy"]
    )

    # Print supplement recommendations
    for supp in result['data']['nutrition_plan']['supplement_recommendations']:
        print(f"{supp['name']}: {supp['dosage']} - {supp['reason']}")

Example Response

{
  "status": "success",
  "data": {
    "analysis_id": "NUT-1A2B3C4D",
    "generated_at": "2026-11-04T10:30:00Z",
    "patient_summary": {
      "age": 34,
      "gender": "female",
      "weight": 62,
      "height": 165,
      "conditions": [],
      "dietary_preferences": "omnivore"
    },
    "nutrition_plan": {
      "nutrition_summary": {
        "daily_calories": 2046,
        "macro_distribution": {
          "protein_g": 153,
          "fat_g": 57,
          "carbs_g": 230,
          "fiber_g": 29
        },
        "key_focus_areas": [
          "Rebuild iron stores (low ferritin and haemoglobin)",
          "Raise vitamin D into the optimal range",
          "Lower LDL cholesterol with fibre and unsaturated fats"
        ],
        "primary_recommendations": [
          "Eat a heme-iron source (red meat, poultry, fish or shellfish) at least once a day",
          "Pair plant iron with vitamin C and keep tea and coffee one hour away from meals",
          "Have oily fish 2-3 times a week and oats, legumes or psyllium every day"
        ]
      },
      "weekly_meal_plan": {
        "monday": {
          "breakfast": {
            "main_dish": "Spinach and mushroom omelette (2 eggs) on wholegrain toast",
            "sides": ["Orange segments", "Cherry tomatoes"],
            "calories": 510,
            "protein": 30,
            "preparation_time": "15 minutes",
            "cost_estimate": "$3-4"
          },
          "morning_snack": {
            "items": ["Pumpkin seeds (30 g)", "Kiwi"],
            "calories": 150
          },
          "lunch": {
            "main_dish": "Lentil and vegetable soup",
            "sides": ["Wholegrain bread", "Red pepper salad with lemon"],
            "calories": 640,
            "protein": 32,
            "preparation_time": "25 minutes",
            "cost_estimate": "$3-5"
          },
          "afternoon_snack": {
            "items": ["Greek yogurt (150 g)", "Strawberries"],
            "calories": 150
          },
          "dinner": {
            "main_dish": "Grilled lean beef steak (150 g)",
            "sides": ["Roasted sweet potato", "Steamed broccoli with lemon"],
            "calories": 600,
            "protein": 45,
            "preparation_time": "30 minutes",
            "cost_estimate": "$6-8"
          }
        }
        // "tuesday" ... "sunday": the same meals as "monday"
      },
      "food_recommendations": {
        "power_foods": [
          {
            "name": "Lean red meat (beef, lamb)",
            "benefits": "Heme iron is absorbed two to three times better than plant iron and refills ferritin fastest; also supplies B12 and zinc.",
            "serving_size": "120-150 g",
            "frequency": "3 times a week",
            "blood_marker_impact": "Ferritin, Hemoglobin"
          },
          {
            "name": "Mussels and clams",
            "benefits": "Among the richest heme-iron foods, with B12 and omega-3 fats.",
            "serving_size": "100 g (cooked, shelled)",
            "frequency": "Once a week",
            "blood_marker_impact": "Ferritin, Hemoglobin"
          }
          // 12-15 foods in a live plan
        ],
        "foods_to_limit": [
          {
            "name": "Tea and coffee with meals",
            "reason": "Tannins and polyphenols can cut iron absorption from a meal by more than half.",
            "alternative": "Drink them at least one hour before or after eating; water or rooibos with meals",
            "impact_on_health": "Slows the rebuilding of ferritin"
          },
          {
            "name": "Processed meats (sausages, salami, bacon)",
            "reason": "High in saturated fat and salt, which raise LDL and blood pressure.",
            "alternative": "Grilled chicken, turkey or lean beef",
            "impact_on_health": "Keeps LDL cholesterol high"
          }
          // 12-15 foods in a live plan
        ],
        "hydration": {
          "daily_water": "3.1 L",
          "timing": "A glass on waking, then evenly through the day; less in the hour before bed",
          "special_considerations": "Keep tea and coffee at least one hour away from iron-rich meals"
        }
      },
      "supplement_recommendations": [
        {
          "name": "Iron bisglycinate",
          "dosage": "25 mg elemental iron a day",
          "timing": "In the morning on an empty stomach, with vitamin C",
          "reason": "Ferritin 14 ng/mL and haemoglobin 11.8 g/dL show depleted iron stores",
          "duration": "3 months, then retest ferritin",
          "contraindications": "Haemochromatosis; take 2 hours apart from calcium, antacids and thyroid medication",
          "source": "evidence_based",
          "priority": "high",
          "blood_marker_addressed": "Ferritin, Hemoglobin",
          "priority_label": "High",
          "source_label": "Evidence-Based"
        },
        {
          "name": "Vitamin D3",
          "dosage": "2000 IU a day",
          "timing": "With the largest meal of the day",
          "reason": "Vitamin D 18 ng/mL is below the 30 ng/mL threshold",
          "duration": "3 months, then retest",
          "contraindications": "Hypercalcaemia, sarcoidosis",
          "source": "evidence_based",
          "priority": "high",
          "blood_marker_addressed": "Vitamin D (25-OH)",
          "priority_label": "High",
          "source_label": "Evidence-Based"
        }
        // 5-8 supplements in a live plan
      ],
      "lifestyle_integration": {
        "meal_prep_tips": [
          "Cook a large pot of lentils or chickpeas on Sunday for three lunches",
          "Keep cut peppers and kiwi ready to add vitamin C to meals",
          "Portion nuts and seeds into small boxes for snacks"
        ],
        "shopping_list": {
          "proteins": ["Lean beef", "Salmon", "Sardines", "Mussels", "Chicken liver", "Eggs"],
          "vegetables": ["Spinach", "Kale", "Broccoli", "Bell peppers", "Tomatoes"],
          "fruits": ["Kiwi", "Oranges", "Strawberries", "Dried apricots"],
          "grains": ["Oats", "Quinoa", "Brown rice", "Wholegrain bread"],
          "dairy_alternatives": ["Greek yogurt", "Kefir", "Cottage cheese"],
          "pantry_staples": ["Lentils", "Chickpeas", "Pumpkin seeds", "Olive oil", "Psyllium husk"]
        },
        "eating_schedule": {
          "breakfast": "07:30-08:30",
          "morning_snack": "10:30",
          "lunch": "12:30-13:30",
          "afternoon_snack": "16:00",
          "dinner": "19:00-20:00"
        },
        "special_occasions": "When eating out, choose grilled meat or fish with salad, and keep coffee for after the meal."
      },
      "monitoring_plan": {
        "weekly_goals": [
          "Heme iron at least 5 days a week",
          "Oily fish twice a week",
          "No tea or coffee within an hour of meals"
        ],
        "tracking_metrics": [
          "Energy levels",
          "Fatigue and breathlessness",
          "Weight",
          "Digestive tolerance of the iron supplement"
        ],
        "adjustment_signs": [
          "Constipation or stomach upset from the iron supplement",
          "No improvement in energy after 6 weeks"
        ],
        "follow_up_tests": [
          "Ferritin, iron and haemoglobin in 8-12 weeks",
          "Vitamin D (25-OH) in 3 months",
          "Lipid panel in 3 months"
        ],
        "expected_improvements": [
          "Ferritin up 15-30 ng/mL within 3 months",
          "Vitamin D above 30 ng/mL within 3 months",
          "LDL down 10-15% within 3 months"
        ]
      },
      "educational_insights": {
        "blood_test_connections": [
          {
            "parameter": "Ferritin",
            "current_value": "14 ng/mL",
            "dietary_impact": "Iron stores are low; heme iron and vitamin C with meals refill them fastest.",
            "foods_that_help": ["Lean red meat", "Mussels", "Chicken liver", "Lentils with peppers"]
          },
          {
            "parameter": "Hemoglobin",
            "current_value": "11.8 g/dL",
            "dietary_impact": "Slightly below range, consistent with iron deficiency; it rises as ferritin recovers.",
            "foods_that_help": ["Beef", "Spinach with lemon", "Eggs"]
          }
          // one entry per relevant blood test parameter
        ],
        "nutritional_science": [
          "Heme iron from animal foods is absorbed at 15-35%, plant iron at 2-20%.",
          "Vitamin C turns plant iron into a form the gut absorbs more easily.",
          "Beta-glucan from oats forms a gel that carries cholesterol-rich bile out of the body."
        ],
        "success_strategies": [
          "Plan the week's meals and shop once",
          "Add a vitamin C food to every iron-rich meal",
          "Retest on schedule to see the progress"
        ]
      },
      "metadata": {
        "generated_at": "2026-11-04T10:30:00",
        "patient_id": "api_1a2b3c4d",
        "bmr": 1320.25,
        "tdee": 2046.39,
        "nutritional_concerns": ["vitamin_d_deficiency", "anemia", "iron_deficiency", "elevated_ldl"],
        "version": "2.2",
        "used_clinic_supplements": false,
        "clinic_supplements_count": 0,
        "report_language": "english",
        "meal_slots": ["breakfast", "morning_snack", "lunch", "afternoon_snack", "dinner"],
        "meal_frequency": "3 meals and 2 snacks a day (breakfast, morning snack, lunch, afternoon snack, dinner)",
        "budget": "moderate",
        "api_provider": "KantestiAI Engine",
        "model": "KantestiAI Engine"
      }
    },
    "labels": {
      "nutrition_summary": "Nutrition Summary",
      "key_focus_areas": "Key Focus Areas",
      "weekly_meal_plan": "Weekly Meal Plan",
      "monday": "Monday",
      "breakfast": "Breakfast",
      "morning_snack": "Morning Snack",
      "main_dish": "Main Dish",
      "supplement_recommendations": "Supplement Recommendations",
      "priority": "Priority",
      "high": "High",
      "evidence_based": "Evidence-Based"
      // ... one entry for every key of nutrition_plan and every priority / source code
    },
    "language": "en",
    "sandbox_mode": false
  },
  "timestamp": "2026-11-04T10:30:00Z",
  "api_version": "1.0.0",
  "message": "Nutrition analysis completed successfully"
}

Response Fields Reference

The live endpoint (/analyze) and the sandbox return this same structure. JSON keys and codes are always in English; all text is in the requested language (sandbox text is always in English). Do not show keys or codes to users: take titles from labels, and show priority_label and source_label for a supplement's priority and source. Every day of weekly_meal_plan, monday to sunday, has the same meals: those of meal_frequency, five by default.

The plan is written in 39 languages: en, de, fr, it, es, pt, ar, he, el, pl, tr, zh, ru, fa, ja, ko, nl, hi, id, th, vi, sv, no, da, fi, cs, ms, af, uk, ro, hu, sk, sl, hr, bg, sr, lv, lt, et. Any other supported language returns the plan and its labels in English.

data object

nutrition_plan object

nutrition_plan.nutrition_summary object

weekly_meal_plan.<day> object

A day has exactly the meals of meal_frequency, and metadata.meal_slots lists them: with 2, only lunch and dinner. Show the meals a day has, not a fixed list of five.

breakfast / lunch / dinner object

food_recommendations.power_foods array item

food_recommendations.foods_to_limit array item

supplement_recommendations array item

Complete cURL Example with Response

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/nutrition/diet-plan/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "demo_user",
    "password": "demo_pass",
    "language": "en",
    "patient": {
      "age": 42,
      "gender": "female",
      "weight": 68,
      "height": 165,
      "activity_level": "moderate",
      "conditions": ["iron_deficiency_anemia"],
      "allergies": ["gluten"],
      "dietary_preferences": ["mediterranean"],
      "dietary_restrictions": ["gluten_free"],
      "liked_foods": ["salmon", "spinach", "quinoa"],
      "disliked_foods": ["liver"],
      "meal_frequency": 5,
      "budget": "moderate",
      "medications": ["ferrous_sulfate"]
    },
    "blood_test": {
      "lab_date": "2025-12-20",
      "parameters": [
        {"short_name": "FER", "result": 12, "unit": "ng/mL"},
        {"short_name": "HGB", "result": 10.8, "unit": "g/dL"},
        {"short_name": "VITD", "result": 22, "unit": "ng/mL"},
        {"short_name": "B12", "result": 320, "unit": "pg/mL"},
        {"short_name": "FOLATE", "result": 8.5, "unit": "ng/mL"}
      ]
    },
    "health_goals": ["increase_energy", "improve_iron_levels"]
  }'

Response:

{
  "status": "success",
  "data": {
    "analysis_id": "NUT-1A2B3C4D",
    "generated_at": "2026-11-04T10:30:00Z",
    "patient_summary": {
      "age": 34,
      "gender": "female",
      "weight": 62,
      "height": 165,
      "conditions": [],
      "dietary_preferences": "omnivore"
    },
    "nutrition_plan": {
      "nutrition_summary": {
        "daily_calories": 2046,
        "macro_distribution": {
          "protein_g": 153,
          "fat_g": 57,
          "carbs_g": 230,
          "fiber_g": 29
        },
        "key_focus_areas": [
          "Rebuild iron stores (low ferritin and haemoglobin)",
          "Raise vitamin D into the optimal range",
          "Lower LDL cholesterol with fibre and unsaturated fats"
        ],
        "primary_recommendations": [
          "Eat a heme-iron source (red meat, poultry, fish or shellfish) at least once a day",
          "Pair plant iron with vitamin C and keep tea and coffee one hour away from meals",
          "Have oily fish 2-3 times a week and oats, legumes or psyllium every day"
        ]
      },
      "weekly_meal_plan": {
        "monday": {
          "breakfast": {
            "main_dish": "Spinach and mushroom omelette (2 eggs) on wholegrain toast",
            "sides": ["Orange segments", "Cherry tomatoes"],
            "calories": 510,
            "protein": 30,
            "preparation_time": "15 minutes",
            "cost_estimate": "$3-4"
          },
          "morning_snack": {
            "items": ["Pumpkin seeds (30 g)", "Kiwi"],
            "calories": 150
          },
          "lunch": {
            "main_dish": "Lentil and vegetable soup",
            "sides": ["Wholegrain bread", "Red pepper salad with lemon"],
            "calories": 640,
            "protein": 32,
            "preparation_time": "25 minutes",
            "cost_estimate": "$3-5"
          },
          "afternoon_snack": {
            "items": ["Greek yogurt (150 g)", "Strawberries"],
            "calories": 150
          },
          "dinner": {
            "main_dish": "Grilled lean beef steak (150 g)",
            "sides": ["Roasted sweet potato", "Steamed broccoli with lemon"],
            "calories": 600,
            "protein": 45,
            "preparation_time": "30 minutes",
            "cost_estimate": "$6-8"
          }
        }
        // "tuesday" ... "sunday": the same meals as "monday"
      },
      "food_recommendations": {
        "power_foods": [
          {
            "name": "Lean red meat (beef, lamb)",
            "benefits": "Heme iron is absorbed two to three times better than plant iron and refills ferritin fastest; also supplies B12 and zinc.",
            "serving_size": "120-150 g",
            "frequency": "3 times a week",
            "blood_marker_impact": "Ferritin, Hemoglobin"
          },
          {
            "name": "Mussels and clams",
            "benefits": "Among the richest heme-iron foods, with B12 and omega-3 fats.",
            "serving_size": "100 g (cooked, shelled)",
            "frequency": "Once a week",
            "blood_marker_impact": "Ferritin, Hemoglobin"
          }
          // 12-15 foods in a live plan
        ],
        "foods_to_limit": [
          {
            "name": "Tea and coffee with meals",
            "reason": "Tannins and polyphenols can cut iron absorption from a meal by more than half.",
            "alternative": "Drink them at least one hour before or after eating; water or rooibos with meals",
            "impact_on_health": "Slows the rebuilding of ferritin"
          },
          {
            "name": "Processed meats (sausages, salami, bacon)",
            "reason": "High in saturated fat and salt, which raise LDL and blood pressure.",
            "alternative": "Grilled chicken, turkey or lean beef",
            "impact_on_health": "Keeps LDL cholesterol high"
          }
          // 12-15 foods in a live plan
        ],
        "hydration": {
          "daily_water": "3.1 L",
          "timing": "A glass on waking, then evenly through the day; less in the hour before bed",
          "special_considerations": "Keep tea and coffee at least one hour away from iron-rich meals"
        }
      },
      "supplement_recommendations": [
        {
          "name": "Iron bisglycinate",
          "dosage": "25 mg elemental iron a day",
          "timing": "In the morning on an empty stomach, with vitamin C",
          "reason": "Ferritin 14 ng/mL and haemoglobin 11.8 g/dL show depleted iron stores",
          "duration": "3 months, then retest ferritin",
          "contraindications": "Haemochromatosis; take 2 hours apart from calcium, antacids and thyroid medication",
          "source": "evidence_based",
          "priority": "high",
          "blood_marker_addressed": "Ferritin, Hemoglobin",
          "priority_label": "High",
          "source_label": "Evidence-Based"
        },
        {
          "name": "Vitamin D3",
          "dosage": "2000 IU a day",
          "timing": "With the largest meal of the day",
          "reason": "Vitamin D 18 ng/mL is below the 30 ng/mL threshold",
          "duration": "3 months, then retest",
          "contraindications": "Hypercalcaemia, sarcoidosis",
          "source": "evidence_based",
          "priority": "high",
          "blood_marker_addressed": "Vitamin D (25-OH)",
          "priority_label": "High",
          "source_label": "Evidence-Based"
        }
        // 5-8 supplements in a live plan
      ],
      "lifestyle_integration": {
        "meal_prep_tips": [
          "Cook a large pot of lentils or chickpeas on Sunday for three lunches",
          "Keep cut peppers and kiwi ready to add vitamin C to meals",
          "Portion nuts and seeds into small boxes for snacks"
        ],
        "shopping_list": {
          "proteins": ["Lean beef", "Salmon", "Sardines", "Mussels", "Chicken liver", "Eggs"],
          "vegetables": ["Spinach", "Kale", "Broccoli", "Bell peppers", "Tomatoes"],
          "fruits": ["Kiwi", "Oranges", "Strawberries", "Dried apricots"],
          "grains": ["Oats", "Quinoa", "Brown rice", "Wholegrain bread"],
          "dairy_alternatives": ["Greek yogurt", "Kefir", "Cottage cheese"],
          "pantry_staples": ["Lentils", "Chickpeas", "Pumpkin seeds", "Olive oil", "Psyllium husk"]
        },
        "eating_schedule": {
          "breakfast": "07:30-08:30",
          "morning_snack": "10:30",
          "lunch": "12:30-13:30",
          "afternoon_snack": "16:00",
          "dinner": "19:00-20:00"
        },
        "special_occasions": "When eating out, choose grilled meat or fish with salad, and keep coffee for after the meal."
      },
      "monitoring_plan": {
        "weekly_goals": [
          "Heme iron at least 5 days a week",
          "Oily fish twice a week",
          "No tea or coffee within an hour of meals"
        ],
        "tracking_metrics": [
          "Energy levels",
          "Fatigue and breathlessness",
          "Weight",
          "Digestive tolerance of the iron supplement"
        ],
        "adjustment_signs": [
          "Constipation or stomach upset from the iron supplement",
          "No improvement in energy after 6 weeks"
        ],
        "follow_up_tests": [
          "Ferritin, iron and haemoglobin in 8-12 weeks",
          "Vitamin D (25-OH) in 3 months",
          "Lipid panel in 3 months"
        ],
        "expected_improvements": [
          "Ferritin up 15-30 ng/mL within 3 months",
          "Vitamin D above 30 ng/mL within 3 months",
          "LDL down 10-15% within 3 months"
        ]
      },
      "educational_insights": {
        "blood_test_connections": [
          {
            "parameter": "Ferritin",
            "current_value": "14 ng/mL",
            "dietary_impact": "Iron stores are low; heme iron and vitamin C with meals refill them fastest.",
            "foods_that_help": ["Lean red meat", "Mussels", "Chicken liver", "Lentils with peppers"]
          },
          {
            "parameter": "Hemoglobin",
            "current_value": "11.8 g/dL",
            "dietary_impact": "Slightly below range, consistent with iron deficiency; it rises as ferritin recovers.",
            "foods_that_help": ["Beef", "Spinach with lemon", "Eggs"]
          }
          // one entry per relevant blood test parameter
        ],
        "nutritional_science": [
          "Heme iron from animal foods is absorbed at 15-35%, plant iron at 2-20%.",
          "Vitamin C turns plant iron into a form the gut absorbs more easily.",
          "Beta-glucan from oats forms a gel that carries cholesterol-rich bile out of the body."
        ],
        "success_strategies": [
          "Plan the week's meals and shop once",
          "Add a vitamin C food to every iron-rich meal",
          "Retest on schedule to see the progress"
        ]
      },
      "metadata": {
        "generated_at": "2026-11-04T10:30:00",
        "patient_id": "api_1a2b3c4d",
        "bmr": 1320.25,
        "tdee": 2046.39,
        "nutritional_concerns": ["vitamin_d_deficiency", "anemia", "iron_deficiency", "elevated_ldl"],
        "version": "2.2",
        "used_clinic_supplements": false,
        "clinic_supplements_count": 0,
        "report_language": "english",
        "meal_slots": ["breakfast", "morning_snack", "lunch", "afternoon_snack", "dinner"],
        "meal_frequency": "3 meals and 2 snacks a day (breakfast, morning snack, lunch, afternoon snack, dinner)",
        "budget": "moderate",
        "api_provider": "KantestiAI Engine",
        "model": "KantestiAI Engine"
      }
    },
    "labels": {
      "nutrition_summary": "Nutrition Summary",
      "key_focus_areas": "Key Focus Areas",
      "weekly_meal_plan": "Weekly Meal Plan",
      "monday": "Monday",
      "breakfast": "Breakfast",
      "morning_snack": "Morning Snack",
      "main_dish": "Main Dish",
      "supplement_recommendations": "Supplement Recommendations",
      "priority": "Priority",
      "high": "High",
      "evidence_based": "Evidence-Based"
      // ... one entry for every key of nutrition_plan and every priority / source code
    },
    "language": "en",
    "sandbox_mode": false
  },
  "timestamp": "2026-11-04T10:30:00Z",
  "api_version": "1.0.0",
  "message": "Nutrition analysis completed successfully"
}

Analyzes 2-20 blood tests and provides detailed comparison with AI-generated narrative insights.

Request Parameters

blood_tests Array Structure

*Either lab_date or results_date is required for each blood test.

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/bloodtest/comparison/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "YOUR_USERNAME",
    "password": "YOUR_PASSWORD",
    "language": "en",
    "blood_tests": [
      {
        "lab_date": "2025-06-15",
        "lab_name": "City Medical Lab",
        "parameters": [
          {"short_name": "HGB", "result": 12.8, "unit": "g/dL"},
          {"short_name": "WBC", "result": 8.2, "unit": "10^9/L"},
          {"short_name": "PLT", "result": 245, "unit": "10^9/L"}
        ]
      },
      {
        "lab_date": "2025-12-15",
        "lab_name": "City Medical Lab",
        "parameters": [
          {"short_name": "HGB", "result": 14.2, "unit": "g/dL"},
          {"short_name": "WBC", "result": 7.1, "unit": "10^9/L"},
          {"short_name": "PLT", "result": 238, "unit": "10^9/L"}
        ]
      }
    ]
  }'

Python Example

import requests
from typing import Dict, List

def compare_blood_tests(
    username: str,
    password: str,
    blood_tests: List[Dict],
    language: str = "en"
) -> Dict:
    """
    Compare multiple blood tests with AI-powered narrative analysis.

    Args:
        username: API username
        password: API password
        blood_tests: List of blood test objects (2-20 tests)
        language: Response language

    Returns:
        dict: Comparison results with AI narrative insights
    """
    url = "https://app.aibloodtestinterpret.com/api/v1/bloodtest/comparison/analyze"

    if len(blood_tests) < 2:
        raise ValueError("Minimum 2 blood tests required")
    if len(blood_tests) > 20:
        raise ValueError("Maximum 20 blood tests allowed")

    payload = {
        "username": username,
        "password": password,
        "language": language,
        "blood_tests": blood_tests
    }

    response = requests.post(url, json=payload, timeout=120)
    response.raise_for_status()
    return response.json()

# Example usage
if __name__ == "__main__":
    tests = [
        {
            "lab_date": "2024-06-15",
            "parameters": [
                {"short_name": "HGB", "result": 12.2, "unit": "g/dL"},
                {"short_name": "CHOL", "result": 235, "unit": "mg/dL"},
                {"short_name": "LDL", "result": 155, "unit": "mg/dL"}
            ]
        },
        {
            "lab_date": "2024-12-15",
            "parameters": [
                {"short_name": "HGB", "result": 14.5, "unit": "g/dL"},
                {"short_name": "CHOL", "result": 185, "unit": "mg/dL"},
                {"short_name": "LDL", "result": 98, "unit": "mg/dL"}
            ]
        }
    ]

    result = compare_blood_tests("your_username", "your_password", tests)

    print(f"Overall trend: {result['data']['comparison_summary']['overall_trend']}")
    for param in result['data']['parameter_analysis']:
        print(f"{param['parameter_name']}: {param['trend_assessment']}")

Response Fields Reference

parameter_analysis Object Structure

Example Response

{
  "api_version": "1.0.0",
  "status": "success",
  "message": "Blood test comparison completed successfully",
  "timestamp": "2025-12-22T01:12:43.057537Z",
  "data": {
    "comparison_id": "CMP-F4ACEE52",
    "tests_compared": 2,
    "date_range": {
      "earliest": "2024-06-15",
      "latest": "2024-12-15",
      "span_days": 183
    },
    "comparison_summary": {
      "overall_trend": "improved",
      "report1_date": "2024-06-15",
      "report2_date": "2024-12-15",
      "time_interval": "183 days between reports",
      "key_findings": [
        "Hemoglobin and RBC levels normalized indicating resolution of anemia",
        "Glucose and HbA1c improved to normal range suggesting better glycemic control",
        "Lipid profile improved with total cholesterol, LDL, HDL, and triglycerides normalized"
      ]
    },
    "parameter_analysis": [
      {
        "parameter_name": "Hemoglobin",
        "report1_value": "12.2 g/dL",
        "report2_value": "14.5 g/dL",
        "change_type": "increased",
        "change_magnitude": "significant",
        "clinical_significance": "Improvement from anemia to normal hemoglobin levels",
        "trend_assessment": "positive"
      },
      {
        "parameter_name": "LDL Cholesterol",
        "report1_value": "155 mg/dL",
        "report2_value": "98 mg/dL",
        "change_type": "decreased",
        "change_magnitude": "significant",
        "clinical_significance": "LDL near optimal range, reducing atherosclerosis risk",
        "trend_assessment": "positive"
      },
      {
        "parameter_name": "HDL Cholesterol",
        "report1_value": "38 mg/dL",
        "report2_value": "55 mg/dL",
        "change_type": "increased",
        "change_magnitude": "significant",
        "clinical_significance": "Improved HDL protective against heart disease",
        "trend_assessment": "positive"
      }
    ],
    "health_assessment": {
      "overall_health_trend": "improved",
      "areas_of_improvement": [
        "Anemia correction",
        "Glycemic control",
        "Lipid profile normalization",
        "Vitamin D and iron status"
      ],
      "areas_of_concern": [],
      "positive_developments": [
        "Resolution of anemia",
        "Normal glucose and HbA1c",
        "Improved cardiovascular risk profile"
      ],
      "risk_factors": [
        "Previous iron deficiency anemia",
        "Prior dyslipidemia",
        "History of impaired glucose metabolism"
      ]
    },
    "recommendations": {
      "immediate_actions": [
        "Continue current supplementation for iron and vitamin D",
        "Maintain glycemic and lipid control with diet and exercise"
      ],
      "follow_up_tests": [
        "Repeat CBC and iron studies in 3 months",
        "Monitor fasting glucose and HbA1c quarterly",
        "Lipid panel recheck in 6 months"
      ],
      "lifestyle_modifications": [
        "Adopt heart-healthy diet low in saturated fats",
        "Increase physical activity to maintain metabolic health"
      ],
      "specialist_referrals": [
        "Consult hematologist if anemia recurs",
        "Endocrinologist referral if glucose control worsens"
      ],
      "monitoring_frequency": "3 months"
    },
    "detailed_interpretation": {
      "sections": [
        {
          "title": "Executive Summary",
          "content": "The patient shows marked improvement in anemia, glucose metabolism, lipid profile, and vitamin status over 6 months."
        },
        {
          "title": "Clinical Recommendations",
          "content": "Continue supplementation and lifestyle measures. Monitor blood counts, iron, glucose, and lipids regularly."
        }
      ]
    },
    "summary": {
      "improved_parameters": 13,
      "stable_parameters": 0,
      "worsened_parameters": 0,
      "overall_trend": "improved"
    },
    "sandbox_mode": false
  }
}

Returns API platform information, available versions, and supported features. No authentication required.

Example Response

{
  "platform": "Kantesti Blood Test Analysis API",
  "versions": ["v6", "v8", "v9", "v10", "v11"],
  "latest_version": "v11",
  "supported_languages": 100,
  "documentation": "https://www.kantesti.net/docs/",
  "status": "operational"
}

Health check endpoint for monitoring. Returns service status. No authentication required.

Example Response

{
  "status": "healthy",
  "timestamp": "2025-12-22T10:30:00Z",
  "uptime": "99.99%"
}

Check your remaining API quota. Requires authentication.

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/quota/check" \
  -H "Content-Type: application/json" \
  -d '{"username": "YOUR_USERNAME", "password": "YOUR_PASSWORD"}'

Example Response

{
  "status": "success",
  "quota": {
    "remaining": 847,
    "total": 1000,
    "reset_date": "2026-01-01",
    "plan": "professional"
  }
}

Generate a comprehensive AI-powered family health risk assessment report. Analyzes family medical history, patient health profiles, and blood test data to identify hereditary risk factors with personalized preventive care recommendations.

Request Parameters (JSON Body)

patient_data Object

family_members Array Item

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/family-health/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "YOUR_USERNAME",
    "password": "YOUR_PASSWORD",
    "patient_data": {
      "name": "Jane Doe",
      "age": 42,
      "gender": "female",
      "dob": "1984-03-15"
    },
    "family_members": [
      {
        "relation": "father",
        "age": 70,
        "conditions": ["hypertension", "type2_diabetes"],
        "deceased": false
      },
      {
        "relation": "mother",
        "age": 67,
        "conditions": ["breast_cancer", "osteoporosis"],
        "deceased": false
      },
      {
        "relation": "paternal_grandfather",
        "conditions": ["coronary_artery_disease"],
        "deceased": true,
        "age_at_death": 72,
        "cause_of_death": "heart_attack"
      }
    ],
    "health_profile": {
      "current_medications": ["metformin"],
      "allergies": ["penicillin"],
      "chronic_conditions": ["prediabetes"],
      "lifestyle_factors": ["sedentary", "non_smoker"]
    },
    "blood_test_data": [
      {"parameter": "glucose", "value": 110, "unit": "mg/dL"},
      {"parameter": "HbA1c", "value": 5.9, "unit": "%"},
      {"parameter": "total_cholesterol", "value": 220, "unit": "mg/dL"}
    ],
    "language": "en"
  }'

Python Example

import requests

def family_health_analyze(username: str, password: str, patient_data: dict,
                          family_members: list = None, health_profile: dict = None,
                          blood_test_data: list = None, language: str = "en"):
    """
    Generate a family health risk assessment report using Kantesti API.
    AI-powered hereditary risk analysis with 100+ language support.

    Args:
        username: API username
        password: API password
        patient_data: Patient info dict with 'age' and 'gender' (required)
        family_members: List of family member dicts with medical history
        health_profile: Patient health profile dict
        blood_test_data: Optional blood test data for correlation
        language: Report language code (default: en)

    Returns:
        dict: Full family health risk assessment report
    """
    url = "https://app.aibloodtestinterpret.com/api/v1/family-health/analyze"

    payload = {
        "username": username,
        "password": password,
        "patient_data": patient_data,
        "language": language
    }

    if family_members:
        payload["family_members"] = family_members
    if health_profile:
        payload["health_profile"] = health_profile
    if blood_test_data:
        payload["blood_test_data"] = blood_test_data

    response = requests.post(url, json=payload, timeout=120)
    response.raise_for_status()
    return response.json()

# Example usage
if __name__ == "__main__":
    result = family_health_analyze(
        username="your_username",
        password="your_password",
        patient_data={"name": "Jane Doe", "age": 42, "gender": "female"},
        family_members=[
            {"relation": "father", "age": 70, "conditions": ["hypertension", "type2_diabetes"]},
            {"relation": "mother", "age": 67, "conditions": ["breast_cancer"]},
        ],
        health_profile={"chronic_conditions": ["prediabetes"]},
        blood_test_data=[{"parameter": "glucose", "value": 110, "unit": "mg/dL"}],
        language="en"
    )
    print(f"Status: {result['status']}")
    report = result["data"]["report_data"]
    print(f"Risk Level: {report['risk_assessment']['overall_risk_level']}")
    for category, risk in report["risk_assessment"].items():
        if isinstance(risk, dict):
            print(f"  {category}: {risk.get('level', 'N/A')} (score: {risk.get('score', 'N/A')})")

C++ Example (libcurl)

#include <iostream>
#include <string>
#include <curl/curl.h>

size_t WriteCallback(void* contents, size_t size, size_t nmemb, std::string* userp) {
    userp->append((char*)contents, size * nmemb);
    return size * nmemb;
}

std::string familyHealthAnalyze(const std::string& username,
                                 const std::string& password,
                                 const std::string& jsonPayload) {
    CURL* curl = curl_easy_init();
    std::string response;

    if (curl) {
        struct curl_slist* headers = NULL;
        headers = curl_slist_append(headers, "Content-Type: application/json");

        curl_easy_setopt(curl, CURLOPT_URL,
            "https://app.aibloodtestinterpret.com/api/v1/family-health/analyze");
        curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);
        curl_easy_setopt(curl, CURLOPT_POSTFIELDS, jsonPayload.c_str());
        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
        curl_easy_setopt(curl, CURLOPT_WRITEDATA, &response);
        curl_easy_setopt(curl, CURLOPT_TIMEOUT, 120L);

        CURLcode res = curl_easy_perform(curl);
        curl_slist_free_all(headers);
        curl_easy_cleanup(curl);
    }
    return response;
}

int main() {
    std::string payload = R"({
        "username": "your_username",
        "password": "your_password",
        "patient_data": {"name": "Jane Doe", "age": 42, "gender": "female"},
        "family_members": [
            {"relation": "father", "age": 70, "conditions": ["hypertension"]},
            {"relation": "mother", "age": 67, "conditions": ["breast_cancer"]}
        ],
        "language": "en"
    })";
    std::string result = familyHealthAnalyze("user", "pass", payload);
    std::cout << result << std::endl;
    return 0;
}

Postman Example

Method: POST
URL: https://app.aibloodtestinterpret.com/api/v1/family-health/analyze
Headers:
  Content-Type: application/json
Body (raw JSON):
{
  "username": "YOUR_USERNAME",
  "password": "YOUR_PASSWORD",
  "patient_data": {
    "age": 42,
    "gender": "female"
  },
  "family_members": [
    {"relation": "father", "age": 70, "conditions": ["hypertension"]}
  ],
  "language": "en"
}

Example Response

{
  "status": "success",
  "data": {
    "report_data": {
      "report_title": "Family Health Risk Assessment Report",
      "executive_summary": "Based on family medical history analysis...",
      "hereditary_risk_analysis": {
        "high_risk": [
          {
            "condition": "Cardiovascular Disease",
            "risk_score": 75,
            "contributing_factors": ["Paternal hypertension", "Grandfather coronary artery disease"],
            "recommendation": "Regular cardiac screening recommended"
          }
        ],
        "moderate_risk": [
          {
            "condition": "Type 2 Diabetes",
            "risk_score": 60,
            "contributing_factors": ["Paternal type 2 diabetes", "Elevated glucose levels"],
            "recommendation": "Annual HbA1c and fasting glucose monitoring"
          }
        ],
        "low_risk": []
      },
      "medication_analysis": {
        "current_medications": ["metformin"],
        "hereditary_sensitivities": [],
        "recommendations": ["Continue metformin for prediabetes management"]
      },
      "blood_test_family_correlation": {
        "correlated_findings": [
          "Elevated glucose (110 mg/dL) correlates with paternal diabetes history",
          "Cholesterol level (220 mg/dL) warrants monitoring given cardiovascular family history"
        ]
      },
      "genetic_screening_recommendations": [
        "BRCA1/BRCA2 genetic testing (maternal breast cancer history)",
        "Cardiovascular genetic panel (strong paternal cardiac history)"
      ],
      "lifestyle_recommendations": [
        "Adopt heart-healthy Mediterranean diet",
        "150 minutes weekly moderate aerobic exercise",
        "Maintain healthy BMI (18.5-24.9)"
      ],
      "family_tree_summary": {
        "paternal_lineage": {"risk_factors": ["hypertension", "type2_diabetes", "coronary_artery_disease"]},
        "maternal_lineage": {"risk_factors": ["breast_cancer", "osteoporosis"]},
        "combined_risk_factors": ["cardiovascular", "metabolic", "oncological"]
      },
      "preventive_care_timeline": [
        {"age_range": "40-45", "screenings": ["Annual mammogram", "Cardiac stress test", "HbA1c every 6 months"]},
        {"age_range": "45-50", "screenings": ["Colonoscopy baseline", "Bone density scan", "Lipid panel annually"]},
        {"age_range": "50+", "screenings": ["Biennial mammogram", "Annual cardiac checkup", "Diabetes screening"]}
      ]
    }
  },
  "message": "Family health risk assessment completed successfully",
  "timestamp": "2026-03-23T10:30:00Z",
  "api_version": "1.0.0"
}

Family Health API Error Codes

Validate your request data before submitting for analysis. No quota is consumed. Use this endpoint to verify your payload structure is correct before calling /analyze.

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/family-health/validate" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "YOUR_USERNAME",
    "password": "YOUR_PASSWORD",
    "patient_data": {"age": 42, "gender": "female"},
    "family_members": [{"relation": "father", "conditions": ["hypertension"]}],
    "language": "en"
  }'

Example Response

{
  "status": "success",
  "data": {
    "validation": {
      "valid": true,
      "data_validation": {"valid": true, "error": null},
      "language_valid": true
    }
  },
  "message": "Validation completed",
  "timestamp": "2026-03-23T10:30:00Z",
  "api_version": "1.0.0"
}

Maps every flagged result in a laboratory panel onto the body. Consumes 1 credit per successful request. A request that fails validation, or whose flagged markers cannot be placed, is not charged.

Request Parameters

Parameter Object Fields

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/body-map/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "YOUR_USERNAME",
    "password": "YOUR_PASSWORD",
    "parameters": [
      {"short_name": "ALT", "long_name": "Alanine aminotransferase", "result": "65", "unit": "U/L", "range_normal_min": 7, "range_normal_max": 45, "evaluation": "high"},
      {"short_name": "AST", "long_name": "Aspartate aminotransferase", "result": "48", "unit": "U/L", "range_normal_min": 8, "range_normal_max": 40, "evaluation": "slightly_high"},
      {"short_name": "TSH", "long_name": "Thyrotropin", "result": "6.2", "unit": "mIU/L", "range_normal_min": 0.4, "range_normal_max": 4.0, "evaluation": "high"}
    ]
  }'

Python Example

import requests

def build_body_map(parameters, username, password, include_image=False):
    """
    Map out-of-range blood test results onto the body.

    Args:
        parameters: List of laboratory result objects
        username: API username
        password: API password
        include_image: Request the rendered illustration (spends image credit)

    Returns:
        dict: Body map block with regions, legend and illustration URLs
    """
    url = "https://app.aibloodtestinterpret.com/api/v1/body-map/analyze"

    response = requests.post(url, json={
        "username": username,
        "password": password,
        "parameters": parameters,
        "include_image": include_image,
    }, timeout=60)
    response.raise_for_status()
    return response.json()

# Example usage
if __name__ == "__main__":
    result = build_body_map(
        parameters=[
            {"short_name": "ALT", "result": "65", "unit": "U/L",
             "range_normal_min": 7, "range_normal_max": 45, "evaluation": "high"},
            {"short_name": "TSH", "result": "6.2", "unit": "mIU/L",
             "range_normal_min": 0.4, "range_normal_max": 4.0, "evaluation": "high"},
        ],
        username="your_username",
        password="your_password",
    )

    body_map = result["data"]["body_map"]
    if result["data"]["all_clear"]:
        print("All clear — nothing flagged.")
    for region in body_map["regions"]:
        severity = "out of range" if region["level"] == 2 else "borderline"
        print(f"  {region['key']}: {severity} ({', '.join(region['markers'])})")
    print(f"Illustration: {body_map['image_url'] or body_map['fallback_url']}")

Example Response

{
  "status": "success",
  "api_version": "1.0.0",
  "message": "Body map generated successfully",
  "data": {
    "body_map": {
      "v": 4,
      "spec": "v4-thy2-liv2",
      "unmapped": 0,
      "image_url": "/static/body_maps/v4-thy2-liv2.webp",
      "fallback_url": "/body-map/v4-thy2-liv2.0123456789abcdef.webp",
      "regions": [
        {"key": "thyroid", "level": 2, "drawn": true, "markers": ["TSH"]},
        {"key": "liver", "level": 2, "drawn": true, "markers": ["ALT", "AST"]}
      ]
    },
    "engine_version": 4,
    "region_keys": ["brain_nerves", "thyroid", "heart_vessels", "liver", "pancreas", "adrenals", "kidneys", "gut", "reproductive", "blood", "immune", "bones", "muscles"],
    "mode": "deterministic",
    "all_clear": false
  },
  "timestamp": "2026-09-18T10:30:00Z"
}

Response Fields Reference

Checks a payload without running the analysis, and reports which of your analyte names the engine recognises. Authentication is required; no quota is consumed, and the endpoint keeps working on an account with no credit left.

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/body-map/validate" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "YOUR_USERNAME",
    "password": "YOUR_PASSWORD",
    "parameters": [
      {"short_name": "ALT", "result": "65", "evaluation": "high"},
      {"short_name": "Unobtainium", "result": "9", "evaluation": "high"}
    ]
  }'

Example Response

{
  "status": "success",
  "api_version": "1.0.0",
  "message": "Payload is valid",
  "data": {
    "valid": true,
    "errors": [],
    "parameter_count": 2,
    "flagged_count": 2,
    "recognised": [
      {"name": "ALT", "region": "liver", "level": 2}
    ],
    "unrecognised": [
      {"name": "Unobtainium", "region": null, "level": 2}
    ]
  },
  "timestamp": "2026-09-18T10:30:00Z"
}

Lists the 13 body regions in canonical order, together with the severity levels. Use it to build your own legend translations.

cURL Example

curl "https://app.aibloodtestinterpret.com/api/v1/body-map/regions"

Example Response

{
  "status": "success",
  "data": {
    "regions": [
      {"key": "brain_nerves", "code": "brn", "order": 0},
      {"key": "thyroid", "code": "thy", "order": 1},
      {"key": "heart_vessels", "code": "hrt", "order": 2},
      {"key": "liver", "code": "liv", "order": 3}
    ],
    "region_keys": ["brain_nerves", "thyroid", "heart_vessels", "liver", "pancreas", "adrenals", "kidneys", "gut", "reproductive", "blood", "immune", "bones", "muscles"],
    "levels": {
      "0": "within range — not shown",
      "1": "borderline (slightly high / slightly low)",
      "2": "out of range (high / low / abnormal)"
    },
    "count": 13
  }
}

Capability metadata: whether the engine is enabled on this deployment, the request limits, the authentication scheme and the full endpoint list.

cURL Example

curl "https://app.aibloodtestinterpret.com/api/v1/body-map/info"

Computes biological blood age and the derived indices from a laboratory panel. Consumes 1 credit per successful request. A request that fails validation, or whose panel yields nothing computable, is not charged.

Request Parameters

The Nine PhenoAge Markers

Send them under whatever names your laboratory printed — matching is by name, in any of the 39 supported report languages, and units are converted automatically.

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/blood-age/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "YOUR_USERNAME",
    "password": "YOUR_PASSWORD",
    "metadata": {"patient_age": "40", "patient_sex": "Male", "lab_date": "2026-09-11"},
    "parameters": [
      {"short_name": "Albumin", "result": 4.4, "unit": "g/dL", "range_normal_min": 3.5, "range_normal_max": 5.0},
      {"short_name": "Creatinine", "result": 0.9, "unit": "mg/dL", "range_normal_min": 0.6, "range_normal_max": 1.2},
      {"short_name": "Glucose", "result": 90, "unit": "mg/dL", "range_normal_min": 70, "range_normal_max": 99},
      {"short_name": "CRP", "result": 1.0, "unit": "mg/L", "range_normal_min": 0, "range_normal_max": 5},
      {"short_name": "Lymphocytes", "result": 30, "unit": "%", "range_normal_min": 20, "range_normal_max": 40},
      {"short_name": "MCV", "result": 90, "unit": "fL", "range_normal_min": 80, "range_normal_max": 100},
      {"short_name": "RDW", "result": 13, "unit": "%", "range_normal_min": 11.5, "range_normal_max": 14.5},
      {"short_name": "ALP", "result": 70, "unit": "U/L", "range_normal_min": 40, "range_normal_max": 130},
      {"short_name": "WBC", "result": 6.0, "unit": "10^9/L", "range_normal_min": 4, "range_normal_max": 11}
    ]
  }'

Python Example

import requests

def biological_blood_age(parameters, metadata, username, password):
    """
    Compute biological blood age from a routine blood panel.

    Args:
        parameters: List of laboratory result objects
        metadata: Report header carrying patient_age and patient_sex
        username: API username
        password: API password

    Returns:
        dict: Blood age block, derived indices, and a flat summary
    """
    url = "https://app.aibloodtestinterpret.com/api/v1/blood-age/analyze"

    response = requests.post(url, json={
        "username": username,
        "password": password,
        "parameters": parameters,
        "metadata": metadata,
    }, timeout=60)
    response.raise_for_status()
    return response.json()

# Example usage
if __name__ == "__main__":
    result = biological_blood_age(
        parameters=[
            {"short_name": "Albumin", "result": 4.4, "unit": "g/dL"},
            {"short_name": "Creatinine", "result": 0.9, "unit": "mg/dL"},
            {"short_name": "Glucose", "result": 90, "unit": "mg/dL"},
            {"short_name": "CRP", "result": 1.0, "unit": "mg/L"},
            {"short_name": "Lymphocytes", "result": 30, "unit": "%"},
            {"short_name": "MCV", "result": 90, "unit": "fL"},
            {"short_name": "RDW", "result": 13, "unit": "%"},
            {"short_name": "ALP", "result": 70, "unit": "U/L"},
            {"short_name": "WBC", "result": 6.0, "unit": "10^9/L"},
        ],
        metadata={"patient_age": "40", "patient_sex": "Male"},
        username="your_username",
        password="your_password",
    )

    summary = result["data"]["summary"]
    if summary["status"] != "ok":
        print(f"No age computed: {summary['status']}")
    else:
        print(f"Chronological: {summary['chronological_age']}")
        print(f"Biological:    {summary['biological_age']} ({summary['source']})")
        print(f"Difference:    {summary['delta_years']:+} years")

    for index in result["data"]["blood_age"]["indices"]:
        print(f"  {index['key']}: {index['value']} {index['unit']} [{index['band']}]")

Example Response

{
  "status": "success",
  "api_version": "1.0.0",
  "message": "Biological blood age computed successfully",
  "data": {
    "blood_age": {
      "version": 1,
      "age": {
        "status": "ok",
        "source": "formula",
        "chrono": 40,
        "pheno": 35.1,
        "delta": -4.9,
        "sex": "m",
        "found": ["albumin", "creatinine", "glucose", "crp", "lymph", "mcv", "rdw", "alp", "wbc"],
        "missing": [],
        "labels": {"albumin": "Albumin", "creatinine": "Creatinine", "glucose": "Glucose"},
        "inputs": {"albumin": 44.0, "creatinine": 79.56, "glucose": 5.0}
      },
      "indices": [
        {"key": "fib4", "group": "liver", "value": 1.12, "unit": "", "band": "ok", "from": ["AST", "ALT", "PLT"]},
        {"key": "egfr", "group": "kidneys", "value": 98.0, "unit": "mL/min/1.73m2", "band": "ok", "from": ["Creatinine"]},
        {"key": "nlr", "group": "immune", "value": 1.8, "unit": "", "band": "ok", "from": ["Neutrophils", "Lymphocytes"]}
      ]
    },
    "engine_version": 1,
    "mode": "deterministic",
    "summary": {
      "status": "ok",
      "source": "formula",
      "chronological_age": 40,
      "biological_age": 35.1,
      "delta_years": -4.9,
      "sex": "m",
      "markers_found": 9,
      "markers_missing": [],
      "indices_count": 3
    }
  },
  "timestamp": "2026-09-18T10:30:00Z"
}

Response Fields Reference

Checks a payload without running the analysis, and reports which of the nine PhenoAge markers your panel supplies and whether a chronological age could be read — the two things that decide whether you will get the complete formula or the partial estimate. Authentication is required; no quota is consumed, and the endpoint keeps working on an account with no credit left.

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/blood-age/validate" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "YOUR_USERNAME",
    "password": "YOUR_PASSWORD",
    "metadata": {"patient_age": "40", "patient_sex": "Male"},
    "parameters": [
      {"short_name": "MCV", "result": 90, "unit": "fL"},
      {"short_name": "WBC", "result": 6.0, "unit": "10^9/L"},
      {"short_name": "Lymphocytes", "result": 30, "unit": "%"}
    ]
  }'

Example Response

{
  "status": "success",
  "api_version": "1.0.0",
  "message": "Payload is valid",
  "data": {
    "valid": true,
    "errors": [],
    "parameter_count": 3,
    "looks_like_blood_panel": true,
    "chronological_age": 40,
    "sex": "m",
    "phenoage_markers_found": ["lymph", "mcv", "wbc"],
    "phenoage_markers_missing": ["albumin", "creatinine", "glucose", "crp", "rdw", "alp"],
    "expected_source": "partial",
    "recognised_markers": ["lymph_pct", "mcv", "wbc"]
  },
  "timestamp": "2026-09-18T10:30:00Z"
}

Lists the nine PhenoAge inputs, every marker the engine can read with its target unit, and the age range the formula applies to.

cURL Example

curl "https://app.aibloodtestinterpret.com/api/v1/blood-age/biomarkers"

Example Response

{
  "status": "success",
  "data": {
    "phenoage_inputs": ["albumin", "creatinine", "glucose", "crp", "lymph", "mcv", "rdw", "alp", "wbc"],
    "phenoage_age_range": {"min": 18, "max": 100},
    "markers": [
      {"key": "albumin", "target_unit": "g/l"},
      {"key": "alp", "target_unit": "u/l"},
      {"key": "alt", "target_unit": "u/l"}
    ],
    "marker_count": 33,
    "name_matching": "Markers are matched by the analyte name your laboratory printed, in any of the supported report languages. You never send these keys."
  }
}

Capability metadata: whether the engine is enabled on this deployment, the two modes and what each costs, the request limits, the authentication scheme and the supported languages.

cURL Example

curl "https://app.aibloodtestinterpret.com/api/v1/blood-age/info"

Interprets one DNA test and returns a comprehensive genetic health report. Send a file as multipart/form-data, or pasted genotype lines as JSON. The upload is parsed while you wait, so an unreadable file is answered at once with 400 and costs nothing. One credit is charged only after the report was produced.

Request Parameters

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/dna-interpretation/analyze?async=1" \
  -u "YOUR_USERNAME:YOUR_PASSWORD" \
  -F "file=@genome_raw_data.txt" \
  -F "language=en" \
  -F 'patient={"age": 41, "sex": "female", "medications": "clopidogrel"}'

# 202 Accepted: {"status": "pending", "job_id": "...", "poll_url": "/api/jobs/...", ...}
curl -u "YOUR_USERNAME:YOUR_PASSWORD" "https://app.aibloodtestinterpret.com/api/jobs/JOB_ID"

Python Example

import time
import requests

BASE = "https://app.aibloodtestinterpret.com"
AUTH = ("YOUR_USERNAME", "YOUR_PASSWORD")


def run(path, poll=True, **kwargs):
    """POST in async mode, then poll /api/jobs/<id> until the report is ready."""
    resp = requests.post(f"{BASE}{path}?async=1", auth=AUTH, timeout=60, **kwargs)
    body = resp.json()
    if resp.status_code != 202:
        return body                      # an error, or a synchronous answer
    while True:
        time.sleep(body.get("poll_interval_ms", 3000) / 1000)
        job = requests.get(f"{BASE}{body['poll_url']}", auth=AUTH, timeout=30).json()
        if job["status"] in ("completed", "failed"):
            return job["result"]["response"]


# 1) DNA test interpretation
with open("genome_raw_data.txt", "rb") as fh:
    dna = run("/api/v1/dna-interpretation/analyze",
              files={"file": fh},
              data={"language": "en", "patient": '{"age": 41, "sex": "female"}'})
report = dna["data"]["report"]
print(report["executive_summary"])
for section in report["sections"]:
    print(section["title"], "-", section["risk_level"])

Example Response

{
  "status": "success",
  "api_version": "1.0.0",
  "message": "DNA test interpretation completed successfully",
  "data": {
    "analysis_id": "DNA-3F9A1C07B2",
    "module": "dna_interpretation",
    "generated_at": "2026-09-23T10:30:00Z",
    "language": "en",
    "source": {
      "kind": "raw", "format": "23andme", "build": "GRCh37",
      "total_records": 638463, "called": 631022, "no_call_rate": 0.0117,
      "inferred_sex": "female", "panel_total": 334, "panel_found": 291,
      "filename": "genome_raw_data.txt", "warnings": []
    },
    "report": {
      "report_type": "dna",
      "title": "Genetic health report",
      "executive_summary": "Array genotyping with good coverage of the clinical panel...",
      "overall_assessment": {"level": "slightly_elevated", "summary": "Mostly typical findings with a few actionable ones."},
      "data_quality": {"source": "23andMe v5 raw file", "markers_analyzed": 291, "coverage_note": "...", "limitations": "..."},
      "sections": [
        {
          "key": "nutrigenomics", "title": "Nutrient metabolism", "risk_level": "slightly_elevated",
          "summary": "Reduced folate cycle activity...",
          "findings": [
            {"gene": "MTHFR", "rsid": "rs1801133", "genotype": "AG", "phenotype": "C677T heterozygous",
             "risk_level": "slightly_elevated", "evidence": "established",
             "explanation": "About 65% of typical enzyme activity.", "recommendation": "Check homocysteine."}
          ]
        }
      ],
      "pharmacogenomics": [
        {"gene": "CYP2C19", "rsids": ["rs4244285"], "predicted_phenotype": "Intermediate metabolizer",
         "affected_drugs": ["clopidogrel", "omeprazole"], "recommendation": "Review before prescribing clopidogrel."}
      ],
      "disease_risks": [], "carrier_status": [], "nutrigenomics": [], "traits": [],
      "lifestyle_recommendations": ["..."], "recommended_tests": [{"test": "Homocysteine", "reason": "MTHFR C677T"}],
      "red_flags": [], "limitations": "Consumer arrays miss rare variants.",
      "disclaimer": "AI-generated clinical decision support for clinician review; not a diagnosis."
    }
  },
  "timestamp": "2026-09-23T10:30:00Z"
}

Response Fields Reference

Parses the upload exactly like /analyze and reports what was found, without an AI call. Authentication is required; no credit is consumed. Use it to check a file before spending a credit.

Example Response

{
  "status": "success",
  "message": "Payload is valid",
  "data": {
    "valid": true,
    "language": "en",
    "source": {"kind": "raw", "format": "ancestrydna", "build": "GRCh37", "panel_total": 334, "panel_found": 287, "warnings": []}
  }
}

Combines a DNA report with an interpreted blood test into one health report. Every link between a genetic finding and a lab value is classified, followed by a risk matrix, priority actions and a monitoring plan. One credit per successful request.

Request Parameters

Python Example

# 2) DNA + blood health report (uses run() and dna from the example above)
blood_test = {
    "parameters": [
        {"short_name": "25-OH D", "result": 18, "unit": "ng/mL", "range_normal_min": 30, "range_normal_max": 100, "evaluation": "low"},
        {"short_name": "Homocysteine", "result": 13.2, "unit": "µmol/L", "evaluation": "high"}
    ]
}
combined = run("/api/v1/dna-blood-report/analyze",
               json={"dna_report": dna, "blood_test": blood_test, "language": "en"})
for link in combined["data"]["report"]["correlations"]:
    print(link["topic"], link["concordance"], link["action"])

Example Response

{
  "status": "success",
  "message": "DNA + blood health report completed successfully",
  "data": {
    "analysis_id": "DNB-8C21E40A9D",
    "module": "dna_blood_report",
    "language": "en",
    "report": {
      "report_type": "dna_blood",
      "executive_summary": "The low vitamin D level matches the GC genotype; homocysteine is borderline, in line with MTHFR C677T.",
      "overall_status": {"level": "watch", "summary": "Two gene-lab matches to act on."},
      "correlations": [
        {"topic": "Vitamin D", "genetic_finding": "GC rs2282679 GT", "lab_finding": "25-OH D 18 ng/mL",
         "concordance": "confirms", "interpretation": "Genetic tendency and lab value agree.", "action": "Supplement and recheck in 12 weeks."}
      ],
      "risk_matrix": [{"area": "Folate cycle", "genetic_risk": "slightly_elevated", "lab_status": "borderline", "combined_assessment": "Watch homocysteine."}],
      "priority_actions": [{"priority": "high", "action": "Start vitamin D3", "why": "Deficient level and GC genotype"}],
      "monitoring_plan": [{"marker": "25-OH vitamin D", "interval": "12 weeks", "reason": "Dose check"}],
      "lifestyle_plan": [], "questions_for_clinician": [], "red_flags": [],
      "limitations": "One blood test; values vary.",
      "disclaimer": "AI-generated clinical decision support for clinician review; not a diagnosis."
    }
  }
}

Response Fields Reference

Builds a personalised supplement plan from the DNA report, the questionnaire answers and, optionally, an interpreted blood test. Your clinic's product catalogue and advisor settings are applied automatically. One credit per successful request.

Request Parameters

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/dna-supplements/analyze?async=1" \
  -u "YOUR_USERNAME:YOUR_PASSWORD" \
  -H "Content-Type: application/json" \
  -d '{
    "dna_report": { ...data.report from the DNA interpretation... },
    "answers": {"diet_type": "vegetarian", "pregnancy": "no", "sun_exposure": "low", "goals": ["energy", "immunity"]},
    "language": "en"
  }'

Python Example

# 3) Supplement plan (uses run(), dna and blood_test from the examples above)
plan = run("/api/v1/dna-supplements/analyze",
           json={"dna_report": dna, "blood_test": blood_test,
                 "answers": {"diet_type": "vegetarian", "pregnancy": "no"},
                 "language": "en"})
for item in plan["data"]["report"]["recommendations"]:
    print(item["name"], item["dose"], item["timing"], "-", item["source"])

Example Response

{
  "status": "success",
  "message": "Supplement plan completed successfully",
  "data": {
    "analysis_id": "SUP-51B7D2E6F0",
    "module": "dna_supplements",
    "language": "en",
    "catalogue": {"mode": "prefer", "products": 24},
    "report": {
      "report_type": "supplement",
      "summary": "Plan built from GC and MTHFR findings, a low vitamin D level and a vegetarian diet.",
      "recommendations": [
        {"name": "Vitamin D3 + K2", "source": "clinic_library", "product": "Vitamin D3 + K2 (Clinic Brand)",
         "form": "softgel", "dose": "2000 IU", "timing": "with lunch", "duration": "12 weeks", "priority": "high",
         "rationale": {"genetic": "GC rs2282679 GT", "lab": "25-OH D 18 ng/mL", "questionnaire": "little sun"},
         "evidence": "established", "cautions": ["Recheck calcium"], "interactions": ["Thiazide diuretics"],
         "retest": "25-OH D in 12 weeks"}
      ],
      "avoid_or_caution": [{"name": "High-dose vitamin A", "reason": "Pregnancy planning"}],
      "uncovered_needs": [],
      "dietary_sources": [{"nutrient": "Folate", "foods": ["lentils", "spinach"]}],
      "retest_plan": [{"marker": "25-OH vitamin D", "when": "12 weeks", "why": "Dose check"}],
      "clinician_review_required": ["Thiazide co-medication"],
      "disclaimer": "AI-generated clinical decision support for clinician review; not a diagnosis."
    }
  }
}

Response Fields Reference

Reads or updates your clinic's product catalogue and the advisor settings. It is the same catalogue as in the clinic panel and in Nutrition Diet AI, so a product added in one place is available everywhere. Authentication is required; no credit is consumed. Send catalogue, settings or both; the catalogue replaces the whole list.

cURL Example

curl -X PUT "https://app.aibloodtestinterpret.com/api/v1/dna-supplements/settings" \
  -u "YOUR_USERNAME:YOUR_PASSWORD" \
  -H "Content-Type: application/json" \
  -d '{
    "catalogue": [
      {"name": "Vitamin D3 + K2", "brand": "Clinic Brand", "form": "softgel", "dosage": "2000 IU / 75 µg", "category": "vitamin"},
      {"name": "Omega-3 EPA/DHA", "brand": "Clinic Brand", "form": "softgel", "dosage": "1000 mg", "category": "omega"}
    ],
    "settings": {"mode": "prefer", "instructions": "Prefer our own brand.", "max_items": 8}
  }'

Extract all text content from uploaded documents using Kantesti ICR technology. Returns structured JSON with document type detection, table extraction, section parsing, and metadata.

Request Parameters

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/icr/v1/extract" \
  -F "username=YOUR_USERNAME" \
  -F "password=YOUR_PASSWORD" \
  -F "language=en" \
  -F "[email protected]"

Python Example

import requests

def icr_extract(file_path: str, username: str, password: str, language: str = "en"):
    """
    Extract text from a document using Kantesti ICR API.
    79% faster and more accurate than traditional OCR.

    Args:
        file_path: Path to the document (PDF, JPG, JPEG, PNG)
        username: API username
        password: API password
        language: Output language code (default: en)

    Returns:
        dict: Structured JSON with document content, tables, sections, metadata
    """
    url = "https://app.aibloodtestinterpret.com/api/icr/v1/extract"

    with open(file_path, "rb") as f:
        files = {"file": (file_path, f)}
        data = {
            "username": username,
            "password": password,
            "language": language
        }

        response = requests.post(url, files=files, data=data, timeout=120)
        response.raise_for_status()
        return response.json()

# Example usage
if __name__ == "__main__":
    result = icr_extract(
        file_path="medical_report.pdf",
        username="your_username",
        password="your_password",
        language="en"
    )
    print(f"Status: {result['status']}")
    print(f"Document Type: {result['data']['document_type']}")
    print(f"Pages: {result['data']['page_count']}")
    for page in result['data']['pages']:
        print(f"  Page {page['page_number']}: {len(page['content']['sections'])} sections")
        for table in page['content'].get('tables', []):
            print(f"    Table: {len(table['rows'])} rows x {len(table['headers'])} cols")

C++ Example (libcurl)

#include <iostream>
#include <string>
#include <curl/curl.h>

size_t WriteCallback(void* contents, size_t size, size_t nmemb, std::string* userp) {
    userp->append((char*)contents, size * nmemb);
    return size * nmemb;
}

std::string icrExtract(const std::string& filePath,
                        const std::string& username,
                        const std::string& password,
                        const std::string& language = "en") {
    CURL* curl = curl_easy_init();
    std::string response;

    if (curl) {
        curl_mime* form = curl_mime_init(curl);
        curl_mimepart* field;

        field = curl_mime_addpart(form);
        curl_mime_name(field, "username");
        curl_mime_data(field, username.c_str(), CURL_ZERO_TERMINATED);

        field = curl_mime_addpart(form);
        curl_mime_name(field, "password");
        curl_mime_data(field, password.c_str(), CURL_ZERO_TERMINATED);

        field = curl_mime_addpart(form);
        curl_mime_name(field, "language");
        curl_mime_data(field, language.c_str(), CURL_ZERO_TERMINATED);

        field = curl_mime_addpart(form);
        curl_mime_name(field, "file");
        curl_mime_filedata(field, filePath.c_str());

        curl_easy_setopt(curl, CURLOPT_URL,
            "https://app.aibloodtestinterpret.com/api/icr/v1/extract");
        curl_easy_setopt(curl, CURLOPT_MIMEPOST, form);
        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
        curl_easy_setopt(curl, CURLOPT_WRITEDATA, &response);
        curl_easy_setopt(curl, CURLOPT_TIMEOUT, 120L);

        CURLcode res = curl_easy_perform(curl);
        curl_mime_free(form);
        curl_easy_cleanup(curl);
    }
    return response;
}

int main() {
    std::string result = icrExtract("medical_report.pdf", "username", "password");
    std::cout << result << std::endl;
    return 0;
}

Example Response

{
  "status": "success",
  "data": {
    "document_type": "blood_test_report",
    "page_count": 1,
    "pages": [
      {
        "page_number": 1,
        "content": {
          "raw_text": "Cologne University Hospital - Blutbild\n\nPatient: Max Mustermann\nDOB: 15.03.1990\nGender: Male\nDate: 10.01.2026\n\nGlucose: 92 mg/dL (74-100)\nALT: 22 U/L (<35)\nCreatinine: 0.9 mg/dL (0.7-1.2)\nWBC: 7.2 10*9/L (3.8-10)\nHGB: 148 g/L (130-175)\nPLT: 230 10*9/L (150-400)",
          "sections": [
            {"type": "header", "content": "Cologne University Hospital - Blutbild"},
            {"type": "paragraph", "content": "Patient: Max Mustermann, DOB: 15.03.1990, Male"},
            {"type": "table", "content": "Blood test results with 6 parameters"}
          ],
          "tables": [
            {
              "headers": ["Test", "Result", "Unit", "Reference Range"],
              "rows": [
                ["Glucose", "92", "mg/dL", "74 - 100"],
                ["ALT", "22", "U/L", "< 35"],
                ["Creatinine", "0.9", "mg/dL", "0.7 - 1.2"],
                ["WBC", "7.2", "10*9/L", "3.8 - 10"],
                ["HGB", "148", "g/L", "130 - 175"],
                ["PLT", "230", "10*9/L", "150 - 400"]
              ]
            }
          ]
        }
      }
    ],
    "metadata": {
      "detected_language": "de",
      "confidence": "high"
    },
    "icr_metadata": {
      "engine": "kantesti-icr",
      "version": "1.0.0",
      "images_processed": 1,
      "timestamp": "2026-02-14T10:30:00Z"
    }
  },
  "credit_cost": 0.5,
  "api_version": "icr-v1",
  "timestamp": "2026-02-14T10:30:00Z"
}

ICR Error Codes

Blood test document analysis using Kantesti Kan engine. Processes blood test documents and extracts structured test parameters, patient information, and metadata.

Request Parameters

cURL Example

curl -X POST "https://app.aibloodtestinterpret.com/api/icr/v1/kan" \
  -F "username=YOUR_USERNAME" \
  -F "password=YOUR_PASSWORD" \
  -F "language=en" \
  -F "file=@blood_test.pdf"

Python Example

import requests

def icr_kan_analyze(file_path: str, username: str, password: str,
                     language: str = "en", pdf_password: str = None):
    """
    Analyze blood test document using Kantesti ICR Kan engine.

    Args:
        file_path: Path to blood test document (PDF, JPG, JPEG, PNG)
        username: API username
        password: API password
        language: Report language code (default: en)
        pdf_password: Password for encrypted PDFs (optional)

    Returns:
        dict: Structured blood test parameters with patient info
    """
    url = "https://app.aibloodtestinterpret.com/api/icr/v1/kan"

    with open(file_path, "rb") as f:
        files = {"file": (file_path, f)}
        data = {
            "username": username,
            "password": password,
            "language": language
        }
        if pdf_password:
            data["pdf_password"] = pdf_password

        response = requests.post(url, files=files, data=data, timeout=120)
        response.raise_for_status()
        return response.json()

# Example usage
if __name__ == "__main__":
    result = icr_kan_analyze(
        file_path="blood_test_report.pdf",
        username="your_username",
        password="your_password",
        language="en"
    )
    print(f"Status: {result['status']}")
    params = result['data']['test_parameters']
    print(f"Parameters found: {len(params)}")
    for param in params:
        print(f"  {param['name']}: {param['value']} {param['unit']} ({param['reference_range']})")

Example Response

{
  "status": "success",
  "data": {
    "institute_info": {
      "name": "Cologne University Hospital"
    },
    "test_metadata": {
      "report_date": "10.01.2026",
      "sample_type": "Blood",
      "test_date": "10.01.2026"
    },
    "test_parameters": [
      {"name": "Glucose (Fasting Blood Sugar)", "value": "92", "unit": "mg/dL", "reference_range": "74 - 100", "type": "range"},
      {"name": "Alanine aminotransferase (ALT)", "value": "22", "unit": "U/L", "reference_range": "< 35", "type": "range"},
      {"name": "Creatinine", "value": "0.9", "unit": "mg/dL", "reference_range": "0.7 - 1.2", "type": "range"},
      {"name": "Cholesterol", "value": "185", "unit": "mg/dL", "reference_range": "< 200", "type": "range"},
      {"name": "TSH", "value": "2.1", "unit": "mIU/L", "reference_range": "0.38 - 5.33", "type": "range"},
      {"name": "WBC (White Blood Cell Count)", "value": "7.2", "unit": "10*9/L", "reference_range": "3.8 - 10", "type": "range"},
      {"name": "RBC (Red Blood Cell Count)", "value": "4.9", "unit": "10*12/L", "reference_range": "4.5 - 5.5", "type": "range"},
      {"name": "HGB (Hemoglobin)", "value": "148", "unit": "g/L", "reference_range": "130 - 175", "type": "range"},
      {"name": "PLT (Platelet Count)", "value": "230", "unit": "10*9/L", "reference_range": "150 - 400", "type": "range"},
      {"name": "HBsAg", "value": "Negative", "unit": "", "reference_range": "", "type": "binary"}
    ],
    "tester_info": {
      "age": "35 (Calculated from 15.03.1990 to 10.01.2026)",
      "gender": "Male"
    },
    "kan_metadata": {
      "engine": "kantesti-kan",
      "version": "1.0.0",
      "language": "en",
      "timestamp": "2026-02-14T10:30:00Z"
    }
  },
  "credit_cost": 0.5,
  "api_version": "icr-v1",
  "timestamp": "2026-02-14T10:30:00Z"
}