Deep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validation

• Published in: Surgical Endoscopy Vol. 36; no. 6; pp. 3811 - 3821
• Main Authors: Chang, Yuan-Yen, Li, Pai-Chi, Chang, Ruey-Feng, Yao, Chih-Da, Chen, Yang-Yuan, Chang, Wen-Yen, Yen, Hsu-Heng
• Format: Journal Article
• Language: English
• Published: New York Springer Science and Business Media LLC 01.06.2022; Springer US; Springer Nature B.V
• Subjects: Abdominal Surgery; Accuracy; Artificial Intelligence; Classification; Computer engineering; Datasets; Deep Learning; Endoscopy; Endoscopy, Gastrointestinal; Endoscopy, Gastrointestinal - methods; Esophagus; Gastroenterology; Gynecology; Hepatology; Hospitals; Humans; Medicine; Medicine & Public Health; Neural Networks, Computer; Proctology; Quality control; Retrospective Studies; Surgery; Ulcers; Deep learning; Artificial intelligence; Endoscopy anatomy

Background Photodocumentation during endoscopy procedures is one of the indicators for endoscopy performance quality; however, this indicator is difficult to measure and audit in the endoscopy unit. Emerging artificial intelligence technology may solve this problem, which requires a large amount of...