Deep Learning Methods in the Imaging of Hepatic and Pancreaticobiliary Diseases.

Journal: Journal of clinical gastroenterology
PMID: 40193287

Abstract

Reports indicate a growing role for artificial intelligence (AI) in the evaluation of pancreaticobiliary and hepatic conditions. A key focus is differentiating between benign and malignant lesions, which is crucial for treatment decisions. AI improves diagnostic accuracy through high sensitivity and specificity, while CNN algorithms enhance image analysis and reduce variability. These advancements aim to match the accuracy of pathologists in cancer detection. In addition, AI aids in identifying diagnostic markers, as early detection is essential. This article reviews the applications of machine learning and deep learning in the diagnosis of hepatic and pancreaticobiliary diseases. Although the use of AI in these specialized areas of gastroenterology is primarily confined to experimental trials, current models demonstrate significant potential for enhancing the detection, evaluation, and treatment planning of hepatic and pancreaticobiliary conditions.

Authors

  • Daryl Ramai
    Department of Anatomical Sciences, St George's University School of Medicine, True Blue, Grenada, WI.
  • Brendan Collins
    Division of Digestive Diseases, University of Cincinnati, Cincinnati, OH.
  • Andrew Ofosu
    University of Cincinnati, Cincinnati, OH.
  • Babu P Mohan
    Division of Gastroenterology and Hepatology, University of Utah, Salt Lake City, UT.
  • Soumya Jagannath
    All India Institute of Medical Sciences, New Delhi, India.
  • James H Tabibian
    David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
  • Mohit Girotra
    Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA.
  • Monique T Barakat
    Stanford University, Palo Alto, CA.

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