Year: 2025
Type: article
Study design: Clinical Trial
Abstract: BACKGROUND: Esophagogastroduodenoscopy (EGD) is essential for diagnosing upper gastrointestinal disorders. Traditional training for novice endoscopists is often inefficient and inconsistent. This study evaluates the effectiveness of an AI-assisted system (EndoAdd) in improving EGD training. METHODS: In a randomized controlled trial, eight novice endoscopists were assigned to either the EndoAdd group or a control group (traditional training). The EndoAdd system provided real-time feedback on blind spots and photodocumentation. Primary outcomes were the number of blind spots, with secondary outcomes including examination time, lesion detection, and photodocumentation completeness. RESULTS: The EndoAdd system exhibited an overall accuracy of 98.0% and a mean area under the curve (AUC) of 0.984. The EndoAdd group had significantly fewer blind spots, improved photodocumentation, and a higher lesion detection rate. Examination time was reduced without compromising diagnostic accuracy. CONCLUSIONS: The AI-assisted EndoAdd system improved novice endoscopist performance, reducing blind spots and enhancing lesion detection. AI systems like EndoAdd show potential in accelerating endoscopy training and improving procedural quality.
Source: Bioengineering
Authors: Zhen Zhang, Bai-Sheng Chen, Ling Du, Quan-Lin Li, Yan Zhu, Pei-Yao Fu, Wen-Zheng Qin, Huan-Kai Shou, Ping-Ting Gao, Xin-Yang Liu, Meng-Jiang He, Zi-Han Geng, Shuo Wang, Ping-Hong Zhou
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W4410818475