新手内镜医师的专家-人工智能协作培训：提高效率之路
Expert-AI Collaborative Training for Novice Endoscopists: A Path to Enhanced Efficiency.
作者信息
Zhang Zhen, Chen Bai-Sheng, Du Ling, Li Quan-Lin, Zhu Yan, Fu Pei-Yao, Qin Wen-Zheng, Shou Huan-Kai, Gao Ping-Ting, Liu Xin-Yang, He Meng-Jiang, Geng Zi-Han, Wang Shuo, Zhou Ping-Hong
机构信息
Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China.
出版信息
Bioengineering (Basel). 2025 May 28;12(6):582. doi: 10.3390/bioengineering12060582.
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.
背景
食管胃十二指肠镜检查（EGD）对于诊断上消化道疾病至关重要。传统的新手内镜医师培训往往效率低下且缺乏一致性。本研究评估了一种人工智能辅助系统（EndoAdd）在改善EGD培训方面的有效性。
方法
在一项随机对照试验中，八名新手内镜医师被分配到EndoAdd组或对照组（传统培训）。EndoAdd系统提供有关盲点和摄影记录的实时反馈。主要结局是盲点数量，次要结局包括检查时间、病变检测和摄影记录完整性。
结果
EndoAdd系统的总体准确率为98.0%，曲线下平均面积（AUC）为0.984。EndoAdd组的盲点明显减少，摄影记录得到改善，病变检测率更高。检查时间缩短，同时不影响诊断准确性。
结论
人工智能辅助的EndoAdd系统提高了新手内镜医师的表现，减少了盲点并增强了病变检测。像EndoAdd这样的人工智能系统在加速内镜培训和提高操作质量方面显示出潜力。
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