A vérvizsgálat API végpontjai

Éles végpont a vérvizsgálat elemzéséhez. Töltsön fel egy vagy több vérvizsgálati képet vagy egy PDF-et, és kapjon strukturált paramétereket, páciens- és laboratóriumi metaadatokat, valamint teljes klinikai értelmezést a 100 támogatott nyelv bármelyikén. Kérésenként 1 kreditet fogyaszt.

Kérés paraméterei

cURL példa

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 példa

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()

# Használati példa
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']})")

Válasz példa

{
  "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"
}

Éles végpont a vérvizsgálat elemzéséhez. Kérésenként 1 kreditet fogyaszt.

Kérés paraméterei

cURL példa

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 példa

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()

# Használati példa
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++ példa (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 konfiguráció

{
  "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"]
        }
      }
    }
  ]
}

Válasz példa

{
  "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"
}

Válasz mezői hivatkozás

Gyökérszint

data.metadata objektum

data.parameters tömb elem

data.interpretation tömb elem

Teljes válasz példa

{
  "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"
}

Éles végpont átfogó egészségügyi pontszám számítással és betegségkockázat-elemzéssel. Ugyanazt a kérést fogadja, mint az /api/v12/18-09-2026/analyze, és az alábbi mezőkkel egészíti ki a választ. Kérésenként 1 kreditet fogyaszt.

cURL példa

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"

További válasz mezők

{
  "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"}
  ]
}

Éles végpont átfogó egészségügyi pontszám számítással és betegségkockázat-elemzéssel.

További válasz mezők

{
  "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"}
  ]
}

Elemzi a vérvizsgálati paraméter trendeket több vizsgálati dátumon át minták azonosítására és egészségügyi betekintések nyújtására.

Kérés paraméterei

analysis_options objektum

blood_tests tömb szerkezete

*Minden vérvizsgálathoz szükséges lab_date vagy results_date.

blood_tests[].parameters tömb szerkezete

cURL példa

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 példa

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()

# Használati példa
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++ példa (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;
}

Válasz mezői hivatkozás

parameter_trends objektum szerkezete

Válasz példa

{
  "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
  }
}

Átfogó táplálkozási és étrend-kiegészítő ajánlásokat generál vérvizsgálati paraméterek és páciensprofil alapján.

Kérés paraméterei

Beteg objektum séma

Betegdemográfiai objektum gender (lásd az értékeket) és activity_level (lásd az értékeket) mezőkkel.

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

cURL példa

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",
      "medical_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 példa

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()

# Használati példa
if __name__ == "__main__":
    patient = {
        "age": 45,
        "gender": "male",
        "weight": 82,
        "height": 178,
        "activity_level": "moderate",
        "medical_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']['supplements']:
        print(f"{supp['name']}: {supp['dosage']} - {supp['reason']}")

Válasz példa

{
  "status": "success",
  "data": {
    "nutrition_plan": {
      "daily_calories": 2100,
      "macros": {
        "protein": {"grams": 105, "percentage": 20},
        "carbohydrates": {"grams": 236, "percentage": 45},
        "fats": {"grams": 82, "percentage": 35}
      },
      "meal_plan": {
        "breakfast": {
          "description": "Oatmeal with berries and walnuts",
          "calories": 420,
          "nutrients": ["fiber", "omega-3", "antioxidants"]
        },
        "lunch": {
          "description": "Grilled salmon salad with olive oil dressing",
          "calories": 550,
          "nutrients": ["omega-3", "protein", "vitamin D"]
        },
        "dinner": {
          "description": "Lean chicken with quinoa and steamed vegetables",
          "calories": 580,
          "nutrients": ["protein", "iron", "fiber"]
        },
        "snacks": [
          {"description": "Greek yogurt with almonds", "calories": 180},
          {"description": "Apple with almond butter", "calories": 200}
        ]
      }
    },
    "supplements": [
      {
        "name": "Vitamin D3",
        "dosage": "2000 IU daily",
        "reason": "Blood levels at 18 ng/mL indicate deficiency (optimal: 30-50 ng/mL)",
        "priority": "high",
        "timing": "With breakfast (fat-containing meal)",
        "duration": "3-6 months, then retest",
        "interactions": []
      },
      {
        "name": "Omega-3 Fish Oil",
        "dosage": "1000mg EPA+DHA daily",
        "reason": "Support cholesterol optimization and cardiovascular health",
        "priority": "medium",
        "timing": "With meals",
        "duration": "Ongoing",
        "interactions": []
      },
      {
        "name": "Coenzyme Q10",
        "dosage": "100mg daily",
        "reason": "Support energy production and heart health",
        "priority": "medium",
        "timing": "With breakfast",
        "duration": "Ongoing",
        "interactions": []
      }
    ],
    "dietary_recommendations": [
      {
        "category": "increase",
        "foods": ["fatty fish", "leafy greens", "nuts", "olive oil"],
        "reason": "Support vitamin D levels and cardiovascular health"
      },
      {
        "category": "decrease",
        "foods": ["processed foods", "red meat", "saturated fats"],
        "reason": "Help lower LDL cholesterol"
      },
      {
        "category": "avoid",
        "foods": ["shellfish", "high-sodium foods"],
        "reason": "Patient allergies and hypertension management"
      }
    ],
    "lifestyle_recommendations": [
      "30 minutes moderate exercise 5 days/week",
      "Sun exposure 15-20 minutes daily for vitamin D",
      "Stress management through meditation or yoga"
    ],
    "follow_up": {
      "retest_date": "2026-03-01",
      "parameters_to_monitor": ["VITD", "CHOL", "HDL", "LDL"]
    }
  },
  "api_version": "v1",
  "timestamp": "2025-12-22T10:30:00Z"
}

Válasz mezői hivatkozás

nutrition_plan.educational_insights objektum

blood_marker_education tömb elem

food_recommendations.power_foods tömb elem

supplement_recommendations tömb elem

Teljes cURL példa válasszal

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": 4,
      "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"]
  }'

Válasz:

{
  "status": "success",
  "data": {
    "nutrition_plan": {
      "daily_calories": 1850,
      "macros": {
        "protein": {"grams": 92, "percentage": 20},
        "carbohydrates": {"grams": 208, "percentage": 45},
        "fats": {"grams": 72, "percentage": 35}
      },
      "educational_insights": {
        "blood_marker_education": [
          {
            "marker": "Ferritin (Low)",
            "explanation": "Ferritin is the storage form of iron in your body. Low levels indicate depleted iron stores, which can lead to fatigue, weakness, and anemia. Your current level of 12 ng/mL is below the optimal range.",
            "normal_range": "Women: 20-200 ng/mL (optimal: 50-150 ng/mL)"
          },
          {
            "marker": "Hemoglobin (Low)",
            "explanation": "Hemoglobin carries oxygen in your red blood cells. Low hemoglobin confirms iron deficiency anemia and explains symptoms like fatigue and shortness of breath.",
            "normal_range": "Women: 12.0-16.0 g/dL"
          },
          {
            "marker": "Vitamin D (Insufficient)",
            "explanation": "Vitamin D is essential for calcium absorption, immune function, and energy. Your level of 22 ng/mL is insufficient; optimal levels are 40-60 ng/mL.",
            "normal_range": "30-100 ng/mL (optimal: 40-60 ng/mL)"
          }
        ],
        "nutrition_principles": [
          "Pair iron-rich foods with vitamin C sources to enhance absorption",
          "Avoid calcium-rich foods and tea/coffee within 2 hours of iron-rich meals",
          "Focus on heme iron sources (meat, fish) which absorb better than plant sources",
          "Include vitamin D-rich foods and consider sun exposure for synthesis"
        ]
      },
      "food_recommendations": {
        "power_foods": [
          {
            "food": "Grass-fed Beef",
            "nutrients": ["heme iron", "B12", "zinc", "protein"],
            "serving": "120g, 3-4x per week",
            "why": "Highest bioavailable iron source to address your ferritin deficiency"
          },
          {
            "food": "Wild Salmon",
            "nutrients": ["vitamin D", "omega-3", "protein", "B12"],
            "serving": "150g, 2-3x per week",
            "why": "Excellent vitamin D source plus omega-3s for inflammation reduction"
          },
          {
            "food": "Spinach with Lemon",
            "nutrients": ["non-heme iron", "folate", "vitamin C"],
            "serving": "100g cooked, daily",
            "why": "Iron plus vitamin C combination maximizes iron absorption"
          },
          {
            "food": "Quinoa",
            "nutrients": ["iron", "protein", "fiber", "magnesium"],
            "serving": "1 cup cooked, daily",
            "why": "Gluten-free grain with good iron content and complete protein"
          },
          {
            "food": "Pumpkin Seeds",
            "nutrients": ["iron", "zinc", "magnesium"],
            "serving": "30g (2 tbsp), daily",
            "why": "Concentrated iron source for snacking, supports energy production"
          }
        ],
        "foods_to_limit": [
          {"food": "Coffee/Tea with meals", "reason": "Tannins block iron absorption by up to 60%"},
          {"food": "Dairy with iron-rich meals", "reason": "Calcium competes with iron for absorption"}
        ]
      }
    },
    "supplement_recommendations": [
      {
        "supplement": "Iron Bisglycinate",
        "dosage": "25mg elemental iron",
        "timing": "Take with vitamin C on empty stomach, 2 hours away from other supplements",
        "duration": "3-6 months, retest ferritin after 3 months",
        "reason": "Ferritin at 12 ng/mL requires supplementation; bisglycinate form is gentle on stomach",
        "priority": "high"
      },
      {
        "supplement": "Vitamin D3",
        "dosage": "2000-4000 IU daily",
        "timing": "With breakfast (fat-containing meal) for absorption",
        "duration": "Ongoing, retest in 3 months to adjust dose",
        "reason": "Level of 22 ng/mL is insufficient; goal is 40-60 ng/mL for optimal health",
        "priority": "high"
      },
      {
        "supplement": "Vitamin C",
        "dosage": "500mg",
        "timing": "Take with iron supplement to enhance absorption",
        "duration": "While taking iron supplements",
        "reason": "Enhances non-heme iron absorption by up to 6x",
        "priority": "medium"
      }
    ],
    "meal_plan": {
      "breakfast": {
        "description": "Quinoa porridge with pumpkin seeds, berries, and coconut milk",
        "calories": 420,
        "key_nutrients": ["iron", "vitamin C", "fiber"]
      },
      "lunch": {
        "description": "Grilled salmon with roasted vegetables and quinoa",
        "calories": 520,
        "key_nutrients": ["vitamin D", "omega-3", "protein"]
      },
      "snack": {
        "description": "Orange slices with pumpkin seeds and dark chocolate",
        "calories": 220,
        "key_nutrients": ["vitamin C", "iron", "magnesium"]
      },
      "dinner": {
        "description": "Grass-fed beef stir-fry with spinach and bell peppers",
        "calories": 480,
        "key_nutrients": ["heme iron", "vitamin C", "B12"]
      }
    },
    "lifestyle_recommendations": [
      "Take iron supplements on an empty stomach for best absorption",
      "Get 15-20 minutes of midday sun exposure for vitamin D synthesis",
      "Space iron supplements 2 hours away from coffee, tea, and calcium",
      "Light exercise can help with energy; avoid intense workouts until iron improves"
    ],
    "follow_up": {
      "retest_date": "2026-03-20",
      "parameters_to_monitor": ["FER", "HGB", "VITD", "iron saturation"],
      "expected_improvements": "Ferritin should increase 20-30 ng/mL, hemoglobin normalize to 12+ g/dL"
    }
  },
  "api_version": "v1",
  "timestamp": "2025-12-20T14:45:22Z"
}

Elemez 2-20 vérvizsgálatot és részletes összehasonlítást nyújt AI által generált narratív betekintésekkel.

Kérés paraméterei

blood_tests tömb szerkezete

*Minden vérvizsgálathoz szükséges lab_date vagy results_date.

cURL példa

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 példa

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()

# Használati példa
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']}")

Válasz mezői hivatkozás

parameter_analysis objektum szerkezete

Válasz példa

{
  "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
  }
}

API platform információkat, elérhető verziókat és támogatott funkciókat ad vissza. Hitelesítés nem szükséges.

Válasz példa

{
  "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"
}

Állapot-ellenőrzés végpont monitorozáshoz. Szolgáltatás állapotát adja vissza. Hitelesítés nem szükséges.

Válasz példa

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

Ellenőrizze fennmaradó API kvótáját. Hitelesítés szükséges.

cURL példa

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

Válasz példa

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

Átfogó AI-alapú családi egészségkockázat-értékelési jelentést generál. Elemzi a családi kórtörténetet, a páciens egészségügyi profiljait és a vérvizsgálati adatokat az örökletes kockázati tényezők azonosítására, személyre szabott megelőző ellátási ajánlásokkal.

Kérés paraméterei (JSON törzs)

patient_data objektum

family_members tömb elem

cURL példa

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 példa

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()

# Használati példa
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++ példa (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 példa

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"
}

Válasz példa

{
  "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": "Családi egészségkockázat-értékelés sikeresen befejezve",
  "timestamp": "2026-03-23T10:30:00Z",
  "api_version": "1.0.0"
}

Családegészség API hibakódok

Ellenőrizze a kérés adatait az elemzésre való benyújtás előtt. Nem fogyaszt kvótát. Használja ezt a végpontot a terhelés szerkezetének ellenőrzésére az /analyze hívása előtt.

cURL példa

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"
  }'

Válasz példa

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

A laboratóriumi panel minden megjelölt eredményét elhelyezi a testen. Sikeres kérésenként 1 kreditet fogyaszt. Az érvényesítésen elbukó kérés, illetve az, amelynek megjelölt markerei nem helyezhetők el, nem kerül kiszámlázásra.

Kérés paraméterei

A paraméterobjektum mezői

cURL példa

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 példa

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()

# Használati példa
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']}")

Válasz példa

{
  "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"
}

Válasz mezői hivatkozás

Ellenőrzi a kérés adatait az elemzés futtatása nélkül, és jelzi, hogy az Ön analitnevei közül melyeket ismeri fel a motor. Hitelesítés szükséges; kvótát nem fogyaszt, és a végpont kredit nélküli fiókon is működik.

cURL példa

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"}
    ]
  }'

Válasz példa

{
  "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"
}

Felsorolja a 13 testtájékot kanonikus sorrendben, a súlyossági szintekkel együtt. Használja saját jelmagyarázat-fordításainak elkészítéséhez.

cURL példa

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

Válasz példa

{
  "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
  }
}

Képesség-metaadatok: engedélyezve van-e a motor ezen a telepítésen, a kéréskorlátok, a hitelesítési séma és a teljes végpontlista.

cURL példa

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

Kiszámítja a biológiai vérkort és a származtatott indexeket egy laboratóriumi panelből. Sikeres kérésenként 1 kreditet fogyaszt. Az érvényesítésen elbukó kérés, illetve az, amelynek paneljéből semmi sem számítható, nem kerül kiszámlázásra.

Kérés paraméterei

A kilenc PhenoAge marker

Küldje be őket azon a néven, ahogyan a laboratóriuma kinyomtatta — az azonosítás név alapján történik, a 39 támogatott leletnyelv bármelyikén, a mértékegységek pedig automatikusan átváltásra kerülnek.

cURL példa

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 példa

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()

# Használati példa
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']}]")

Válasz példa

{
  "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"
}

Válasz mezői hivatkozás

Ellenőrzi a kérés adatait az elemzés futtatása nélkül, és jelzi, hogy a kilenc PhenoAge marker közül melyeket tartalmazza a panelje, valamint hogy sikerült-e naptári életkort kiolvasni — ez a két dolog dönti el, hogy a teljes képletet vagy a részleges becslést kapja. Hitelesítés szükséges; kvótát nem fogyaszt, és a végpont kredit nélküli fiókon is működik.

cURL példa

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": "%"}
    ]
  }'

Válasz példa

{
  "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"
}

Felsorolja a kilenc PhenoAge bemenetet, a motor által olvasható összes markert a célmértékegységével együtt, valamint azt az életkori tartományt, amelyre a képlet vonatkozik.

cURL példa

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

Válasz példa

{
  "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."
  }
}

Képesség-metaadatok: engedélyezve van-e a motor ezen a telepítésen, a két mód és azok költsége, a kéréskorlátok, a hitelesítési séma és a támogatott nyelvek.

cURL példa

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

Egy DNS-tesztet értelmez, és átfogó genetikai egészségjelentést ad vissza. Fájlt multipart/form-data formátumban, beillesztett genotípussorokat pedig JSON-ként küldhet. A feltöltés feldolgozása még a kérés során megtörténik, így az olvashatatlan fájlra azonnal 400 válasz érkezik, és nem kerül semmibe. Az 1 kreditet csak a jelentés elkészülte után számoljuk fel.

Kérés paraméterei

cURL példa

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 példa

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                      # hiba vagy szinkron válasz
    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) DNS-teszt értelmezése
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"])

Válasz példa

{
  "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"
}

Válasz mezői hivatkozás

Pontosan úgy dolgozza fel a feltöltést, mint az /analyze, és AI-hívás nélkül jelzi, mit talált. Hitelesítés szükséges; kreditet nem fogyaszt. Használja arra, hogy egy fájlt még a kredit felhasználása előtt ellenőrizzen.

Válasz példa

{
  "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": []}
  }
}

Egy DNS-jelentést és egy értelmezett vérvizsgálatot egyetlen egészségjelentésbe egyesít. A genetikai lelet és a laborérték közötti minden kapcsolatot besorolja, majd kockázati mátrixot, kiemelt teendőket és monitorozási tervet ad. Sikeres kérésenként 1 kreditet fogyaszt.

Kérés paraméterei

Python példa

# 2) DNS + vér egészségjelentés (a fenti példa run() függvényét és dna változóját használja)
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"])

Válasz példa

{
  "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."
    }
  }
}

Válasz mezői hivatkozás

Személyre szabott étrend-kiegészítő tervet készít a DNS-jelentésből, a kérdőív válaszaiból és opcionálisan egy értelmezett vérvizsgálatból. A klinikája termékkatalógusát és a tanácsadó beállításait automatikusan alkalmazza. Sikeres kérésenként 1 kreditet fogyaszt.

Kérés paraméterei

cURL példa

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": { ...a DNS-teszt-értelmezés data.report objektuma... },
    "answers": {"diet_type": "vegetarian", "pregnancy": "no", "sun_exposure": "low", "goals": ["energy", "immunity"]},
    "language": "en"
  }'

Python példa

# 3) Étrend-kiegészítő terv (a fenti példák run() függvényét, valamint dna és blood_test változóját használja)
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"])

Válasz példa

{
  "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."
    }
  }
}

Válasz mezői hivatkozás

Lekérdezi vagy frissíti a klinikája termékkatalógusát és a tanácsadó beállításait. Ez ugyanaz a katalógus, mint a klinikai panelen és a Táplálkozási AI szolgáltatásban, így az egy helyen felvett termék mindenhol elérhető. Hitelesítés szükséges; kreditet nem fogyaszt. Küldje el a catalogue vagy a settings mezőt, illetve mindkettőt; a katalógus a teljes listát lecseréli.

cURL példa

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}
  }'

Kinyeri az összes szöveges tartalmat a feltöltött dokumentumokból a Kantesti ICR technológia segítségével. Strukturált JSON-t ad vissza dokumentumtípus-észleléssel, táblázat-kinyeréssel, szakasz-elemzéssel és metaadatokkal.

Kérés paraméterei

cURL példa

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 példa

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()

# Használati példa
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++ példa (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;
}

Válasz példa

{
  "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 hibakódok

Vérvizsgálati dokumentum-elemzés a Kantesti Kan motor segítségével. Feldolgozza a vérvizsgálati dokumentumokat, és kinyeri a strukturált vizsgálati paramétereket, a páciens információit és a metaadatokat.

Kérés paraméterei

cURL példa

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 példa

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()

# Használati példa
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']})")

Válasz példa

{
  "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"
}