Building Machine Learning Models in Gastrointestinal Endoscopy.

Journal: Gastrointestinal endoscopy clinics of North America
PMID: 40021229

Abstract

The current landscape of machine learning models in GI endoscopy is fraught with considerable variability in methodologies and quality, posing challenges for validation and generalization. To ensure the effective integration of AI in clinical practice, it is crucial to develop and validate models rigorously across diverse and representative datasets. This involves standardizing reference standards, ensuring thorough external validation, using representative patient populations, and incorporating a range of image qualities. Addressing these methodological discrepancies will enhance the reliability and robustness of AI models, thereby facilitating their adoption and improving patient care in GI endoscopy.

Authors

  • Giulio Antonelli
    Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy.
  • Tom Eelbode
    Department 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. Electronic address: tom.eelbode@kuleuven.be.
  • Touka Elsaman
    Department of Biomedical Sciences, Humanitas Research Hospital and University, Via Manzoni 56, Rozzano, Milano 20089, Italy.
  • Mrigya Sharma
    Medical Intern, GMERS Medical College, Vadodara, India.
  • Raf Bisschops
    Department of Gastroenterology and Hepatology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium; Department of Translational Research in Gastrointestinal Diseases (TARGID), Catholic University Leuven, Herestraat 49, 3000, Leuven, Belgium. Electronic address: raf.bisschops@uzleuven.be.
  • Cesare Hassan
    Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy.

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