Kantesti · AI Blood Test Analyzer

🧬 Next-Generation Medical AI Platform | IQ 152 | 2.38T Parameters

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🚀 V8 API Now Available - Revolutionary Medical AI

Experience superhuman intelligence in blood test analysis with our breakthrough V8 technology

🌟 Overview

AI Blood Test Analyzer by PIYA.AI represents a paradigm shift in medical diagnostics. Our V8 neural architecture achieves an unprecedented IQ of 152, surpassing human specialists in pattern recognition and disease detection.

🎯 V8 Breakthrough Features

Superhuman AI MENSA-verified IQ 152 Exceeds specialists

Lightning Speed V8 optimized engine Results in seconds

Unmatched Precision Advanced ICR tech Low-quality scan support

Early Detection Predictive analytics Pattern recognition

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🛠️ API Integration Guide

🔑 Authentication

Base URL: https://app.aibloodtestinterpret.com/api/v8/31-03-2025/analyze
Authentication: Basic Auth (Username + Password)

📋 Request Parameters

Parameter	Type	Required	Description
username	string	✅ Yes	Your API username
password	string	✅ Yes	Your API password
file	file	✅ Yes	Blood test file (PDF/JPG/PNG, max 10MB)
language	string	✅ Yes	ISO 639-1 code (e.g., en, tr, de)
output_format	string	❌ No	json (default), pdf, markdown

🌍 Multi-Language Code Examples

🐍 Python

import requests
import json
from datetime import datetime

# V8 API Endpoint
url = 'https://app.aibloodtestinterpret.com/api/v8/31-03-2025/analyze'

# Authentication
username = 'your_username'
password = 'your_password'

# Request data
data = {
    'username': username,
    'password': password,
    'language': 'en',  # Change to your language
    'output_format': 'json'
}

# Upload files
files = []
with open('blood_test.pdf', 'rb') as f:
    files.append(('file', ('blood_test.pdf', f, 'application/pdf')))
    
    # Make request
    response = requests.post(url, data=data, files=files)

# Handle response
if response.status_code == 200:
    result = response.json()
    if result['status'] == 'success':
        print(f"✅ Analysis complete!")
        print(f"🧠 AI Confidence: {result['data']['ai_metrics']['confidence_score']}")
        print(f"⚡ Processing Time: {result['data']['ai_metrics']['processing_time_ms']}ms")
        
        # Save results
        with open(f'analysis_{datetime.now().strftime("%Y%m%d_%H%M%S")}.json', 'w') as f:
            json.dump(result, f, indent=2)
else:
    print(f"❌ Error: {response.json()['message']}")

🟨 JavaScript (Node.js)

const fs = require('fs');
const axios = require('axios');
const FormData = require('form-data');

// V8 API Endpoint
const url = 'https://app.aibloodtestinterpret.com/api/v8/31-03-2025/analyze';

// Authentication
const username = 'your_username';
const password = 'your_password';

async function analyzeBloodTest() {
  try {
    // Create form data
    const formData = new FormData();
    formData.append('username', username);
    formData.append('password', password);
    formData.append('language', 'en');
    formData.append('output_format', 'json');
    
    // Add file
    const fileStream = fs.createReadStream('blood_test.pdf');
    formData.append('file', fileStream);
    
    // Make request
    const response = await axios.post(url, formData, {
      headers: formData.getHeaders()
    });
    
    if (response.data.status === 'success') {
      console.log('✅ Analysis complete!');
      console.log(`🧠 AI Confidence: ${response.data.data.ai_metrics.confidence_score}`);
      console.log(`⚡ Processing Time: ${response.data.data.ai_metrics.processing_time_ms}ms`);
      
      // Save results
      fs.writeFileSync(
        `analysis_${Date.now()}.json`,
        JSON.stringify(response.data, null, 2)
      );
    }
  } catch (error) {
    console.error('❌ Error:', error.response?.data?.message || error.message);
  }
}

analyzeBloodTest();

☕ Java

import java.io.*;
import java.net.http.*;
import java.nio.file.*;
import java.util.*;

public class BloodTestAnalyzer {
    private static final String API_URL = "https://app.aibloodtestinterpret.com/api/v8/31-03-2025/analyze";
    
    public static void main(String[] args) throws Exception {
        // Authentication
        String username = "your_username";
        String password = "your_password";
        
        // Create multipart request
        String boundary = UUID.randomUUID().toString();
        HttpRequest request = HttpRequest.newBuilder()
            .uri(URI.create(API_URL))
            .header("Content-Type", "multipart/form-data; boundary=" + boundary)
            .POST(buildMultipartBody(boundary, username, password, "blood_test.pdf", "en"))
            .build();
        
        // Send request
        HttpClient client = HttpClient.newHttpClient();
        HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
        
        if (response.statusCode() == 200) {
            System.out.println("✅ Analysis complete!");
            // Parse JSON response
            Files.writeString(Path.of("analysis_result.json"), response.body());
        } else {
            System.err.println("❌ Error: " + response.body());
        }
    }
    
    private static HttpRequest.BodyPublisher buildMultipartBody(
        String boundary, String username, String password, String filePath, String language
    ) throws IOException {
        var byteArrays = new ArrayList<byte[]>();
        
        // Add form fields
        byteArrays.add(("--" + boundary + "\r\n" +
            "Content-Disposition: form-data; name=\"username\"\r\n\r\n" + 
            username + "\r\n").getBytes());
            
        byteArrays.add(("--" + boundary + "\r\n" +
            "Content-Disposition: form-data; name=\"password\"\r\n\r\n" + 
            password + "\r\n").getBytes());
            
        byteArrays.add(("--" + boundary + "\r\n" +
            "Content-Disposition: form-data; name=\"language\"\r\n\r\n" + 
            language + "\r\n").getBytes());
        
        // Add file
        byteArrays.add(("--" + boundary + "\r\n" +
            "Content-Disposition: form-data; name=\"file\"; filename=\"" + filePath + "\"\r\n" +
            "Content-Type: application/pdf\r\n\r\n").getBytes());
        byteArrays.add(Files.readAllBytes(Paths.get(filePath)));
        byteArrays.add("\r\n".getBytes());
        
        byteArrays.add(("--" + boundary + "--\r\n").getBytes());
        
        return HttpRequest.BodyPublishers.ofByteArrays(byteArrays);
    }
}

🔷 C#/.NET

using System;
using System.Net.Http;
using System.Threading.Tasks;
using System.IO;
using Newtonsoft.Json;

class BloodTestAnalyzer
{
    private static readonly string ApiUrl = "https://app.aibloodtestinterpret.com/api/v8/31-03-2025/analyze";
    
    static async Task Main(string[] args)
    {
        using var client = new HttpClient();
        using var form = new MultipartFormDataContent();
        
        // Authentication
        form.Add(new StringContent("your_username"), "username");
        form.Add(new StringContent("your_password"), "password");
        form.Add(new StringContent("en"), "language");
        form.Add(new StringContent("json"), "output_format");
        
        // Add file
        var fileContent = new ByteArrayContent(File.ReadAllBytes("blood_test.pdf"));
        fileContent.Headers.ContentType = new System.Net.Http.Headers.MediaTypeHeaderValue("application/pdf");
        form.Add(fileContent, "file", "blood_test.pdf");
        
        // Send request
        var response = await client.PostAsync(ApiUrl, form);
        
        if (response.IsSuccessStatusCode)
        {
            var jsonResponse = await response.Content.ReadAsStringAsync();
            dynamic result = JsonConvert.DeserializeObject(jsonResponse);
            
            Console.WriteLine("✅ Analysis complete!");
            Console.WriteLine($"🧠 AI Confidence: {result.data.ai_metrics.confidence_score}");
            Console.WriteLine($"⚡ Processing Time: {result.data.ai_metrics.processing_time_ms}ms");
            
            // Save results
            File.WriteAllText($"analysis_{DateTime.Now:yyyyMMdd_HHmmss}.json", jsonResponse);
        }
        else
        {
            Console.WriteLine($"❌ Error: {response.StatusCode}");
        }
    }
}

🟦 PHP

<?php
// V8 API Endpoint
$url = 'https://app.aibloodtestinterpret.com/api/v8/31-03-2025/analyze';

// Authentication
$username = 'your_username';
$password = 'your_password';

// Prepare the file
$file_path = 'blood_test.pdf';
$cFile = new CURLFile($file_path, 'application/pdf', basename($file_path));

// Request data
$data = array(
    'username' => $username,
    'password' => $password,
    'language' => 'en',
    'output_format' => 'json',
    'file' => $cFile
);

// Initialize cURL
$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, $url);
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, $data);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);

// Execute request
$response = curl_exec($ch);
$httpCode = curl_getinfo($ch, CURLINFO_HTTP_CODE);
curl_close($ch);

// Handle response
if ($httpCode == 200) {
    $result = json_decode($response, true);
    if ($result['status'] == 'success') {
        echo "✅ Analysis complete!\n";
        echo "🧠 AI Confidence: " . $result['data']['ai_metrics']['confidence_score'] . "\n";
        echo "⚡ Processing Time: " . $result['data']['ai_metrics']['processing_time_ms'] . "ms\n";
        
        // Save results
        file_put_contents('analysis_' . date('Ymd_His') . '.json', $response);
    }
} else {
    echo "❌ Error: HTTP $httpCode\n";
    echo $response;
}
?>

💎 Ruby

require 'net/http'
require 'uri'
require 'json'

# V8 API Endpoint
uri = URI.parse('https://app.aibloodtestinterpret.com/api/v8/31-03-2025/analyze')

# Authentication
username = 'your_username'
password = 'your_password'

# Create multipart request
request = Net::HTTP::Post.new(uri)
boundary = "----WebKitFormBoundary#{SecureRandom.hex(8)}"
request['Content-Type'] = "multipart/form-data; boundary=#{boundary}"

# Build body
post_body = []
post_body << "--#{boundary}\r\n"
post_body << "Content-Disposition: form-data; name=\"username\"\r\n\r\n"
post_body << "#{username}\r\n"

post_body << "--#{boundary}\r\n"
post_body << "Content-Disposition: form-data; name=\"password\"\r\n\r\n"
post_body << "#{password}\r\n"

post_body << "--#{boundary}\r\n"
post_body << "Content-Disposition: form-data; name=\"language\"\r\n\r\n"
post_body << "en\r\n"

post_body << "--#{boundary}\r\n"
post_body << "Content-Disposition: form-data; name=\"file\"; filename=\"blood_test.pdf\"\r\n"
post_body << "Content-Type: application/pdf\r\n\r\n"
post_body << File.read('blood_test.pdf')
post_body << "\r\n--#{boundary}--\r\n"

request.body = post_body.join

# Send request
http = Net::HTTP.new(uri.host, uri.port)
http.use_ssl = true
response = http.request(request)

# Handle response
if response.code == '200'
  result = JSON.parse(response.body)
  if result['status'] == 'success'
    puts "✅ Analysis complete!"
    puts "🧠 AI Confidence: #{result['data']['ai_metrics']['confidence_score']}"
    puts "⚡ Processing Time: #{result['data']['ai_metrics']['processing_time_ms']}ms"
    
    # Save results
    File.write("analysis_#{Time.now.strftime('%Y%m%d_%H%M%S')}.json", response.body)
  end
else
  puts "❌ Error: #{response.code}"
  puts response.body
end

🦀 Rust

use reqwest;
use serde_json::Value;
use std::fs;
use tokio;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // V8 API Endpoint
    let url = "https://app.aibloodtestinterpret.com/api/v8/31-03-2025/analyze";
    
    // Authentication
    let username = "your_username";
    let password = "your_password";
    
    // Read file
    let file_bytes = fs::read("blood_test.pdf")?;
    let file_part = reqwest::multipart::Part::bytes(file_bytes)
        .file_name("blood_test.pdf")
        .mime_str("application/pdf")?;
    
    // Create multipart form
    let form = reqwest::multipart::Form::new()
        .text("username", username.to_string())
        .text("password", password.to_string())
        .text("language", "en")
        .text("output_format", "json")
        .part("file", file_part);
    
    // Send request
    let client = reqwest::Client::new();
    let response = client
        .post(url)
        .multipart(form)
        .send()
        .await?;
    
    // Handle response
    if response.status().is_success() {
        let json: Value = response.json().await?;
        if json["status"] == "success" {
            println!("✅ Analysis complete!");
            println!("🧠 AI Confidence: {}", json["data"]["ai_metrics"]["confidence_score"]);
            println!("⚡ Processing Time: {}ms", json["data"]["ai_metrics"]["processing_time_ms"]);
            
            // Save results
            let timestamp = chrono::Local::now().format("%Y%m%d_%H%M%S");
            fs::write(
                format!("analysis_{}.json", timestamp),
                serde_json::to_string_pretty(&json)?
            )?;
        }
    } else {
        println!("❌ Error: {}", response.status());
    }
    
    Ok(())
}

🐹 Go

package main

import (
    "bytes"
    "encoding/json"
    "fmt"
    "io"
    "io/ioutil"
    "mime/multipart"
    "net/http"
    "os"
    "time"
)

func main() {
    // V8 API Endpoint
    url := "https://app.aibloodtestinterpret.com/api/v8/31-03-2025/analyze"
    
    // Create buffer and multipart writer
    var b bytes.Buffer
    w := multipart.NewWriter(&b)
    
    // Add form fields
    w.WriteField("username", "your_username")
    w.WriteField("password", "your_password")
    w.WriteField("language", "en")
    w.WriteField("output_format", "json")
    
    // Add file
    file, err := os.Open("blood_test.pdf")
    if err != nil {
        panic(err)
    }
    defer file.Close()
    
    fw, err := w.CreateFormFile("file", "blood_test.pdf")
    if err != nil {
        panic(err)
    }
    io.Copy(fw, file)
    
    // Close multipart writer
    w.Close()
    
    // Create request
    req, err := http.NewRequest("POST", url, &b)
    if err != nil {
        panic(err)
    }
    req.Header.Set("Content-Type", w.FormDataContentType())
    
    // Send request
    client := &http.Client{}
    resp, err := client.Do(req)
    if err != nil {
        panic(err)
    }
    defer resp.Body.Close()
    
    // Read response
    body, err := ioutil.ReadAll(resp.Body)
    if err != nil {
        panic(err)
    }
    
    // Handle response
    if resp.StatusCode == 200 {
        var result map[string]interface{}
        json.Unmarshal(body, &result)
        
        if result["status"] == "success" {
            fmt.Println("✅ Analysis complete!")
            data := result["data"].(map[string]interface{})
            metrics := data["ai_metrics"].(map[string]interface{})
            fmt.Printf("🧠 AI Confidence: %v\n", metrics["confidence_score"])
            fmt.Printf("⚡ Processing Time: %vms\n", metrics["processing_time_ms"])
            
            // Save results
            timestamp := time.Now().Format("20060102_150405")
            filename := fmt.Sprintf("analysis_%s.json", timestamp)
            ioutil.WriteFile(filename, body, 0644)
        }
    } else {
        fmt.Printf("❌ Error: HTTP %d\n", resp.StatusCode)
        fmt.Println(string(body))
    }
}

🔶 Swift

import Foundation

// V8 API Endpoint
let url = URL(string: "https://app.aibloodtestinterpret.com/api/v8/31-03-2025/analyze")!

// Create multipart request
var request = URLRequest(url: url)
request.httpMethod = "POST"

let boundary = UUID().uuidString
request.setValue("multipart/form-data; boundary=\(boundary)", forHTTPHeaderField: "Content-Type")

// Build body
var body = Data()

// Add form fields
let fields = [
    "username": "your_username",
    "password": "your_password",
    "language": "en",
    "output_format": "json"
]

for (key, value) in fields {
    body.append("--\(boundary)\r\n".data(using: .utf8)!)
    body.append("Content-Disposition: form-data; name=\"\(key)\"\r\n\r\n".data(using: .utf8)!)
    body.append("\(value)\r\n".data(using: .utf8)!)
}

// Add file
let fileURL = URL(fileURLWithPath: "blood_test.pdf")
let fileData = try! Data(contentsOf: fileURL)

body.append("--\(boundary)\r\n".data(using: .utf8)!)
body.append("Content-Disposition: form-data; name=\"file\"; filename=\"blood_test.pdf\"\r\n".data(using: .utf8)!)
body.append("Content-Type: application/pdf\r\n\r\n".data(using: .utf8)!)
body.append(fileData)
body.append("\r\n--\(boundary)--\r\n".data(using: .utf8)!)

request.httpBody = body

// Send request
let task = URLSession.shared.dataTask(with: request) { data, response, error in
    guard let data = data,
          let httpResponse = response as? HTTPURLResponse,
          httpResponse.statusCode == 200 else {
        print("❌ Error: \(error?.localizedDescription ?? "Unknown error")")
        return
    }
    
    // Parse response
    if let json = try? JSONSerialization.jsonObject(with: data) as? [String: Any],
       let status = json["status"] as? String,
       status == "success" {
        print("✅ Analysis complete!")
        
        if let data = json["data"] as? [String: Any],
           let metrics = data["ai_metrics"] as? [String: Any] {
            print("🧠 AI Confidence: \(metrics["confidence_score"] ?? "")")
            print("⚡ Processing Time: \(metrics["processing_time_ms"] ?? "")ms")
        }
        
        // Save results
        let timestamp = DateFormatter.localizedString(from: Date(), dateStyle: .none, timeStyle: .medium)
        let filename = "analysis_\(timestamp).json"
        try? data.write(to: URL(fileURLWithPath: filename))
    }
}

task.resume()

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📊 Response Structure

🔍 View Complete JSON Response Example

{
    "interpretation": [
        {
            "shortcode": "introduction",
            "title": "Introduction",
            "subsections": [
                {
                    "shortcode": "introduction_summary",
                    "subtitle": "General Summary of Blood Test",
                    "items": [
                        {
                            "item": "This laboratory evaluation includes a comprehensive metabolic panel, complete blood count, kidney and liver function tests..."
                        },
                        {
                            "item": "Key findings include optimal glucose metabolism and renal function..."
                        }
                    ]
                }
            ]
        },
        {
            "shortcode": "overall_health_assessment",
            "title": "Overall Health Assessment",
            "subsections": [
                {
                    "shortcode": "overall_health_assessment_overview",
                    "subtitle": "Comprehensive Overview of Patient's Health Status",
                    "items": [
                        {
                            "item": "All major complete blood count parameters fall within normal reference intervals..."
                        }
                    ]
                }
            ]
        }
        // ... additional sections
    ],
    "metadata": {
        "lab_name": "LABORATOR DE ANALIZE MEDICALE MEDO",
        "lab_date": "2024-09-13",
        "lab_city": "",
        "lab_country": "",
        "patient_age": "52",
        "patient_sex": "M",
        "results_date": "2024-09-23"
    },
    "parameters": [
        {
            "short_name": "Hemoglobina",
            "long_name": "Hemoglobin",
            "category": "Complete Blood Count",
            "result": 14.3,
            "unit": "g/dl",
            "type": "range",
            "evaluation": "optimal",
            "range_normal_min": 13.1,
            "range_normal_max": 17.2,
            "range_optimal_min": 13.88,
            "range_optimal_max": 15.76,
            "short_description": "Hemoglobin is the protein in red blood cells that carries oxygen.",
            "long_description": "<p>Hemoglobin enables red blood cells to transport oxygen...</p>"
        },
        {
            "short_name": "Glicemie",
            "long_name": "Blood Glucose",
            "category": "Biochemistry",
            "result": 82.96,
            "unit": "mg/dl",
            "type": "range",
            "evaluation": "optimal",
            "range_normal_min": 55,
            "range_normal_max": 115,
            "range_optimal_min": 66,
            "range_optimal_max": 92
        },
        {
            "short_name": "Proteine urinare",
            "long_name": "Protein in Urine",
            "category": "Urinalysis",
            "result": "negative",
            "type": "binary",
            "evaluation": "good",
            "good_result": "Negative",
            "bad_result": "Positive",
            "short_description": "Detects protein presence in urine indicating kidney issues."
        }
        // ... 40+ additional parameters analyzed
    ],
    "ai_metrics": {
        "confidence_score": 0.98,
        "processing_time_ms": 780,
        "model_version": "v8.0.3",
        "parameters_used": "2.38T",
        "iq_level": 152
    }
}

📋 Response Field Descriptions

Section	Field	Description
interpretation	Array of analysis sections	Comprehensive health insights organized by topic
↳	title	Section title (e.g., "Risk Factors", "Recommendations")
↳	subsections	Detailed subsections with specific findings
↳	items	Individual insights and observations
metadata	Patient & lab information	Context about the test and patient
↳	patient_age/sex	Demographics for personalized analysis
↳	lab_date	When the test was performed
parameters	Array of test results	All analyzed biomarkers with detailed data
↳	evaluation	AI assessment: optimal/normal/abnormal
↳	range_*	Reference ranges (normal & optimal)
↳	type	Parameter type: range or binary
ai_metrics	Performance data	AI analysis quality indicators

---

🌍 Supported Languages (75+)

🇺🇸 English (en)	🇹🇷 Türkçe (tr)	🇩🇪 Deutsch (de)	🇫🇷 Français (fr)	🇪🇸 Español (es)
🇮🇹 Italiano (it)	🇵🇹 Português (pt)	🇳🇱 Nederlands (nl)	🇷🇺 Русский (ru)	🇵🇱 Polski (pl)
🇨🇳 中文 (zh)	🇯🇵 日本語 (ja)	🇰🇷 한국어 (ko)	🇸🇦 العربية (ar)	🇮🇳 हिन्दी (hi)

+ 60 more languages supported

---

🚀 Performance Metrics

⚡ V8 Technology Benchmarks

Neural Network Performance:
  ├── IQ Level: 152 (MENSA Verified)
  ├── Parameters: 2.38 Trillion
  ├── Processing Speed: <60 seconds
  ├── Accuracy Rate: 98.7%
  └── Early Detection: +35% improvement

Infrastructure Metrics:
  ├── Uptime: 99.99% SLA
  ├── Response Time: <300ms
  ├── Concurrent Requests: 500/min
  ├── Global CDN: 15 locations
  └── Zero-Persistence: Immediate purge

Security Standards:
  ├── Encryption: TLS 1.3 + AES-256
  ├── Compliance: HIPAA, GDPR, SOC 2
  ├── Authentication: Multi-factor available
  ├── Data Retention: Zero persistence
  └── Audit: Real-time monitoring

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💎 Why Choose Kantesti

🏆 Industry Recognition 🥇 Best AI Healthcare Platform 2024 - TechCrunch 🏅 Innovation Excellence Award - World AI Summit ⭐ 5/5 Rating - 50,000+ Healthcare Professionals

🤝 Enterprise Partners

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🔧 Rate Limits & Pricing

Plan	Requests/Min	Daily Quota	Monthly Quota	Price
Free Trial	10	10	10	€0
Standard	60	1,000	10,000	€49/mo
Professional	120	5,000	50,000	€149/mo
Enterprise	500	Custom	Custom	Contact

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📞 Support & Resources

🎯 Quick Links

• 📧 Email: support@piya.ai
• 📱 WhatsApp: +49 177 497 4039
• 🌐 Website: www.kantesti.net
• 🔗 API Portal: app.aibloodtestinterpret.com/api-report
• 🎮 Demo: www.kantesti.net/#ai-blood-test-analyzer-free

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Pioneering the Future of Medical Diagnostics with Superhuman AI

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