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Prospective pilot evaluation of a deep learning model for kidney stone detection on CT using a web-based workflow

Coşku Öksüz1

  • 1Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Izmir Bakırçay University, İzmir, Turkey. cosku.oksuz@bakircay.edu.tr.

International Urology and Nephrology
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Summary

This study prospectively evaluated an AI kidney stone detection model in a simulated radiology workflow, demonstrating high accuracy and reliability. The findings support AI integration into clinical practice for improved diagnostic efficiency.

Keywords:
Clinical decision supportClinical workflow integrationDeep learningKidney stone detectionNon-contrast CTProspective pilot studyUrolithiasis

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Diagnostic Technology

Background:

  • Non-contrast abdominal CT is crucial for rapid kidney stone detection in emergency settings.
  • Increasing imaging volumes necessitate AI support to manage radiologist workload.
  • Limited prospective evaluations of AI models in realistic clinical workflows exist.

Purpose of the Study:

  • To prospectively evaluate the performance, usability, and workflow compatibility of a deep learning-based kidney stone detection model.
  • To assess AI model performance within a web-based platform simulating routine radiology practice.
  • To establish a framework for AI system transition from retrospective validation to prospective deployment.

Main Methods:

  • A dual-stage convolutional neural network was developed and validated.
  • The AI model was integrated into a secure, browser-based platform for prospective evaluation.
  • Three radiologists annotated 5,152 anonymized CT slices over six months, logging human-AI interactions.

Main Results:

  • The AI model achieved high diagnostic performance: 97.83% accuracy, 94.64% sensitivity, 98.27% specificity, 88.50% precision, and a Cohen's kappa of 0.90.
  • Concordance between radiologists and the AI model showed increasing stability throughout the pilot.
  • The simulated workflow enabled forward-in-time evaluation without direct PACS/RIS integration.

Conclusions:

  • The AI kidney stone detection model demonstrated strong diagnostic performance and stability in a simulated real-world setting.
  • The study provides a reproducible framework for prospective AI pilot deployment in radiology.
  • Pilot-stage evaluation is crucial for clinical implementation and regulatory approval of AI tools.