Endoscopic image analysis assisted by machine learning: Algorithmic advancements and clinical uses

Clinical gastrointestinal endoscopy has significantly advanced owing to machine learning techniques, which have produced novel instruments and approaches for early-stage disease diagnosis, categorization, and therapy. Machine learning applications in gastrointestinal endoscopy, such as image identification, lesion detection, pathological categorization, and surgical aid, are examined in this minireview. We examine the potential of machine learning to improve treatment regimens, lower misdiagnosis rates, and increase diagnostic accuracy by evaluating previous research. In addition, this study discusses current issues such clinical applicability, model generalization, and data privacy. It also suggests future research directions to help clinicians and researchers in the field of gastrointestinal endoscopy.