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Kidney stone is a major global health concern that can have serious consequences for individuals if it isn’t detected and treated early. Clinical methods of diagnosis like test reports, CT scans, and ultrasound reports depend heavily on the expertise of the doctor, which can cause results to vary. In this review study, various papers are examined to analyze how the integration of machine learning in kidney stone disease can improve diagnostic efficiency and accuracy. The results demonstrate how machine learning has the potential to transform kidney stone detection, despite challenges like model transparency and workflow integration.

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A Review Study on Automated Detection of Kidney Stone Using Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Tarul,
  • Arunima Jaiswal,
  • Nitin Sachdeva

摘要

Kidney stone is a major global health concern that can have serious consequences for individuals if it isn’t detected and treated early. Clinical methods of diagnosis like test reports, CT scans, and ultrasound reports depend heavily on the expertise of the doctor, which can cause results to vary. In this review study, various papers are examined to analyze how the integration of machine learning in kidney stone disease can improve diagnostic efficiency and accuracy. The results demonstrate how machine learning has the potential to transform kidney stone detection, despite challenges like model transparency and workflow integration.