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Explainable artificial intelligence (XAI): closing the gap between image analysis and navigation in complex invasive

S O'Sullivan1, M Janssen2, Andreas Holzinger3,4

  • 1Department of Urology, University Hospital of Münster (UKM), Muenster, Germany. sosullivan810@gmail.com.

World Journal of Urology
|January 27, 2022
PubMed
Summary
This summary is machine-generated.

Conventional cystoscopy for bladder cancer (BCa) diagnosis has risks of misdiagnosis. Robot-assisted cystoscopy with explainable AI (XAI) offers a safer, automated alternative, improving diagnostic accuracy and workflow for urologists.

Keywords:
Autonomous endoscopyAutonomous surgeryBladder cancer detectionBladder cancer diagnosisCystoscopyRobotic endoscopy

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Area of Science:

  • Urology
  • Medical Technology
  • Artificial Intelligence

Background:

  • Cystoscopy is the standard for diagnosing bladder cancer (BCa), but its findings can be difficult to interpret, leading to potential misdiagnoses.
  • The rates of false negatives and false positives in current cystoscopy practices are not well-defined, posing risks of under- or over-diagnosis.
  • These diagnostic inaccuracies can lead to delayed cancer treatment or unnecessary procedures.

Purpose of the Study:

  • To explore the potential of explainable artificial intelligence (XAI) robot-assisted cystoscopes to improve bladder cancer diagnosis.
  • To establish a framework for semi-autonomous cystoscopy that can be a model for other endoscopic and surgical procedures.
  • To address the limitations of conventional cystoscopy by introducing automation and enhancing diagnostic accuracy.

Main Methods:

  • Review of current cystoscopy practices and their limitations in diagnosing bladder cancer.
  • Conceptualization of XAI robot-assisted cystoscopy systems.
  • Discussion of automation levels and the 'human-in-the-loop' approach for safety in semi-autonomous procedures.

Main Results:

  • XAI robot-assisted cystoscopy has the potential to mitigate the risks and flaws associated with conventional cystoscopy.
  • Semi-autonomous cystoscopy can establish standards for automation in medical procedures.
  • A human supervisor remains essential ('human-in-the-loop') for patient safety in robotic cystoscopy.

Conclusions:

  • Robot-assisted cystoscopy offers a safer, more accurate method for bladder cancer diagnosis compared to traditional methods.
  • The development of standards for semi-autonomous cystoscopy can pave the way for automation in other medical fields.
  • Automated diagnostic cystoscopy allows urologists to review findings efficiently, potentially delegating routine procedures to specialized nurses.