Artificial intelligence-assisted esophagogastroduodenoscopy improves procedure quality for endoscopists in early stages of training

Shannon Melissa Chan; Daniel Chan; Hon Chi Yip; Markus Wolfgang Scheppach; Ray Lam; Stephen Kk Ng; Enders Kwok Wai Ng; Philip W Chiu

doi:10.1055/a-2547-6645

Artificial intelligence-assisted esophagogastroduodenoscopy improves procedure quality for endoscopists in early stages of training

Endosc Int Open. 2025 Apr 15:13:a25476645. doi: 10.1055/a-2547-6645. eCollection 2025.

Authors

Shannon Melissa Chan¹, Daniel Chan², Hon Chi Yip¹, Markus Wolfgang Scheppach³, Ray Lam¹, Stephen Kk Ng¹, Enders Kwok Wai Ng¹, Philip W Chiu¹

Affiliations

¹ Department of Surgery, The Chinese University of Hong Kong, Hong Kong, Hong Kong.
² Surgery, UNSW St George & Sutherland, Kogarah, Australia.
³ Internal Medicine III - Gastroenterology, University of Augsburg Faculty of Medicine, Augsburg, Germany.

Abstract

Background and study aims: Completeness of esophagagogastroduodenoscopy (EGD) varies among endoscopists, leading to a high miss rate for gastric neoplasms. This study aimed to determine the effect of the Cerebro real-time artificial intelligence (AI) system on completeness of EGD for endoscopists in early stages of training.

Patients and methods: The AI system was built with CNN and Motion Adaptive Temporal Feature Aggregation (MA-TFA). A prospective sequential cohort study was conducted. Endoscopists were taught about the standardized EGD protocol to examine 27 sites. Then, each subject performed diagnostic EGDs per protocol (control arm). After completion of the required sample size, subjects performed diagnostic EGDs with assistance of the AI (study arm). The primary outcome was the rate of completeness of EGD. Secondary outcomes included overall inspection time, individual site inspection time, completeness of photodocumentation, and rate of positive pathologies.

Results: A total of 466 EGDs were performed with 233 in each group. Use of AI significantly improved completeness of EGD [mean (SD) (92.6% (6.2%) vs 71.2% (16.8%)]; P <0.001 (95% confidence interval 19.2%-23.8%, SD 0.012). There was no difference in overall mean (SD) inspection time [765.5 (338.4) seconds vs 740.4 (266.2); P =0.374]. Mean (SD) number of photos for photo-documentation significantly increased in the AI group [26.9 (0.4) vs 10.3 (4.4); P <0.001]. There was no difference in detection rates for pathologies in the two groups [8/233 (3.43%) vs 5/233 (2.16%), P =0.399].

Conclusions: Completeness of EGD examination and photodocumentation by endoscopists in early stages of are improved by the AI-assisted software Cerebro.

Keywords: Image and data processing, documentatiton; Quality and logistical aspects; Quality management; Training.

The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).