Identify renal failure using AI

Below is a free classifier to identify renal failure. Just upload your image, and our AI will predict if there's a renal issue or not - in just seconds.

renal failure identifier

Please upload an X-ray image. For informational purposes only. Please speak to a professional before making any medical decisions.

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Get started

    import nyckel
    
    credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
    nyckel.invoke("renal-failure-identifier", "your_image_url", credentials)
                

    fetch('https://www.nyckel.com/v1/functions/renal-failure-identifier/invoke', {
        method: 'POST',
        headers: {
            'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
            'Content-Type': 'application/json',
        },
        body: JSON.stringify(
            {"data": "your_image_url"}
        )
    })
    .then(response => response.json())
    .then(data => console.log(data));
                

    curl -X POST \
        -H "Content-Type: application/json" \
        -H "Authorization: Bearer YOUR_BEARER_TOKEN" \
        -d '{"data": "your_image_url"}' \
        https://www.nyckel.com/v1/functions/renal-failure-identifier/invoke
                

How this classifier works

To start, upload your image. Our AI tool will then predict if there's a renal issue or not.

This pretrained image model uses the Renal Failure dataset and has 4 labels, including Tumor, Cyst, Stone, or Normal.

We'll also show a confidence score (the higher the number, the more confident the AI model is around if there's a renal issue or not).

Whether you're just curious or building renal failure detection into your application, we hope our classifier proves helpful.

Recommended Classifiers

Need to identify renal failure at scale?

Get API or Zapier access to this classifier for free. It's perfect for:



  • Healthcare: Diagnose renal failure from X-ray images. It analyzes radiographic patterns to detect signs of kidney dysfunction.

  • Radiology Departments: Screen patients for potential kidney issues. It prioritizes cases for further examination by nephrologists.

  • Emergency Medicine: Quickly assess patients with suspected kidney problems. It helps triage cases in high-volume emergency settings.

  • Medical Research: Study progression of renal diseases over time. It analyzes large datasets of X-rays to identify patterns and trends.

  • Telemedicine: Provide remote diagnosis for patients in rural areas. It enables specialists to review X-rays and offer timely consultations.

  • Veterinary Medicine: Detect kidney problems in animals using X-rays. It supports veterinarians in diagnosing renal issues in pets and livestock.

  • Medical Education: Train new radiologists to recognize signs of renal failure. It provides a tool for practice and assessment of diagnostic skills.

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