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Objectives: Artificial intelligence (AI) uses deep learning functionalities that may enhance the detection of early gastric cancer during endoscopy. An AI‐based endoscopic system for upper endoscopy was recently developed in Japan. We aim to validate this AI‐based system in a Singaporean cohort. Methods: There were 300 de‐identified still images prepared from endoscopy video files obtained from subjects that underwent gastroscopy in National University Hospital (NUH). Five specialists and 6 non‐specialists (trainees) from NUH were assigned to read and categorize the images into "neoplastic" or "non‐neoplastic." Results were then compared with the readings performed by the endoscopic AI system. Results: The mean accuracy, sensitivity, and specificity for the 11 endoscopists were 0.847, 0.525, and 0.872, respectively. These values for the AI‐based system were 0.777, 0.591, and 0.791, respectively. While AI in general did not perform better than endoscopists on the whole, in the subgroup of high‐grade dysplastic lesions, only 29.1% were picked up by the endoscopist rating, but 80% were classified as neoplastic by AI (P = 0.0011). The average diagnostic time was also faster in AI compared with endoscopists (677.1 s vs 42.02 s (P < 0.001). Conclusion: We demonstrated that an AI system developed in another health system was comparable in diagnostic accuracy in the evaluation of static images. AI systems are faster and not fatigable and may have a role in augmenting human diagnosis during endoscopy. With more advances in AI and larger studies to support its efficacy it would likely play a larger role in screening endoscopy in future. [ABSTRACT FROM AUTHOR]

Laparoscopic appendectomy is being performed increasingly, worldwide. To the resident, it provides a valuable opportunity to master basic laparoscopic skills. Studies have shown that laparoscopic appendectomy performed by residents is safe. However, to date, there is no clear evidence for the minimum number of these operations required to achieving proficiency and safety. The aim of this study is to assess the outcome of laparoscopic appendectomies performed by surgical residents and to evaluate the effect of learning curve on patient outcome. All patients undergoing laparoscopic appendectomies performed by six residents during the study period were reviewed. Data on patient demographics, clinical and histological diagnosis, and outcome variables including operative duration, conversion to open surgery, complications, and length of stay were analyzed. We evaluated the effect of the learning curve by dividing patients into two groups: a first group consisting of the initial 20 patients and a second group consisting of the next 20 patients operated upon independently by each resident. Variables were analyzed to determine any difference between the two groups. Three hundred six patients with the clinical diagnosis of acute appendicitis underwent laparoscopic appendectomy by residents. Mean operative time was 83.8 min. Of patients, 14.6% required conversion to open surgery. Mean length of hospitalization was 2.82 days. Operative duration and complication rate were significantly reduced with increasing experience of residents. The length of hospital stay and conversion rate to open surgery remained unchanged. Operative duration and complications can be reduced with increasing experience of a resident. [ABSTRACT FROM AUTHOR]

The article describes the case of a 57-year-old man complaining of discomfort in swallowing. Several medical procedures including endoscopy and endoscopic ultrasound revealed submucosal tumors (SMT) in his mid esophagus. The SMT was removed using the submucosal tunneling endoscopic resection, using endoscopic clips for closure. The examination showed leiomyoma. Images of the procedure are also presented.