AI data handling platform for kidney stone disease diagnosis based on CT scan images

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AI data handling platform for kidney stone disease diagnosis based on CT scan images
  • Title: AI data handling platform for kidney stone disease diagnosis based on CT scan images
  • Category: Healthcare
  • Description: Preliminary diagnosis of kidney stone characteristics performs a key role in taking efficient therapeutic approaches.

AI data handling platform for kidney stone disease diagnosis based on CT scan images

Preliminary diagnosis of kidney stone characteristics performs a key role in taking efficient therapeutic approaches. Current diagnosis method relies on radiologist interpretation of CT scans. Clinicians spend a great deal of time in image interpretation, which places a great challenge on the efficacy of image diagnosis. Our solution can help in interpreting data retrieved from CT scanning images. We therefore see a significant opportunity to enhance patient care, reduce wait times for radiologist interpretation, and improve both patient and doctor outcomes.

Using ground truth based machine learning to interpret CT scans, we develop an intelligent software application capable of determining kidney stone presence, size, aspect ratio and other geometrical features, as well as the probable composition. Our solution offers the advantage of physical and chemical interpretation of the kidney stones using explainable computer vision algorithms. The solution includes:

  1. Testing the location of the detected stone in the kidney and the urinary system from CT scan image.
  2. Approximate the size of the stone based on the three largest diameters in every aspect of the final identified count of the stone.
  3. Estimate the location of the stone in the kidneys and the urinary system based on the slide number of the CT image and confirm with radiologist.
  4. Identifying the material and the type of the stone from among 6 different common types of stones.

This technology will advance the presence of AI in the medical field by use of image-guided therapy, both in machine vision and in spectroscopic correlative analysis of kidney stones.