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Pitfalls in training and validation of deep learning systems.

Tom Eelbode1, Pieter Sinonquel2, Frederik Maes1

  • 1Department of Electrical Engineering (ESAT/PSI), KU Leuven, Kasteelpark Arenberg 10/2446, 3001, Leuven, Belgium; Medical Imaging Research Center (MIRC), UZ Leuven, Herestraat 49, 3000, Leuven, Belgium.

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Deep learning shows promise in endoscopy for automated detection and diagnosis. This review addresses common pitfalls in training and validating these AI systems, offering guidelines for unbiased, generalizable clinical applications.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Endoscopy

Background:

  • Deep learning applications in endoscopic journals have rapidly increased.
  • Deep learning offers potential for automated detection, diagnosis, and quality improvement in endoscopy.
  • The interdisciplinary nature of these studies complicates value and applicability assessment.

Purpose of the Study:

  • To discuss pitfalls and common misconducts in training and validating deep learning systems for endoscopy.
  • To propose practical guidelines for data acquisition and handling to ensure unbiased AI systems.
  • To present considerations for the correct validation and comparison of artificial intelligence (AI) systems in clinical practice.

Main Methods:

  • Review of current literature on deep learning in endoscopy.
  • Identification of common errors and biases in AI model development and validation.
  • Formulation of practical guidelines and recommendations.

Main Results:

  • Identified significant challenges in data acquisition, preprocessing, and validation methodologies.
  • Highlighted common biases leading to poor generalization in clinical settings.
  • Proposed a framework for robust AI system development and evaluation.

Conclusions:

  • Adherence to proposed guidelines is crucial for developing reliable and generalizable deep learning tools for endoscopy.
  • Standardized validation protocols are needed for accurate comparison of AI systems.
  • Addressing pitfalls in AI development will enhance clinical integration and utility.