Machine Learning in Gastrointestinal Tract Imaging: A Comprehensive Review of Techniques and Applications

Abstract

GI imaging modalities provide vital diagnostic information, yet their manual interpretation remains laborious. Deep learning approaches have achieved high accuracy, but clinical adoption is impeded by data limitations and trust concerns. We systematically map algorithmic trends to specific GI imaging techniques, quantify the relationship between dataset size and model performance, and evaluate translational enablers such as federated learning and explainable AI.

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