Liu Ruide, Yuan Xianglei, Huang Kaide, Luo Qi, Zhou Nuoya, Wu Chuncheng, Peng Tingfa, Zhang Wanhong, Bi Xiaogang, Chen Xin, Wei Wei, Zhu Yinong, Zhang Lifan, Yi Zhang, Hu Bing
Department of Gastroenterology and Hepatology, Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, No.37, Guo Xue Alley, Wuhou District, Chengdu, Sichuan, China.
Center of Intelligent Medicine, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu, Sichuan, China.
Surg Endosc. 2025 Jun 20. doi: 10.1007/s00464-025-11883-2.
Endoscopic submucosal dissection (ESD) was an important minimally invasive procedure for treating early esophageal cancer, where the surrounding mucosal incision (SMI) was a crucial yet challenging phase. This study aimed to develop an artificial intelligence (AI) model for marks detection and incision guide line prediction during SMI, and to evaluate its performance compared to multicenter endoscopists.
Images were extracted at one frame per second from video clips and divided into 3 datasets: training dataset, internal and external test dataset. Twenty-two endoscopists participated in the comparison with the model, including eight senior and fourteen junior endoscopists. Multiple objective indicators and a marks-guide line subjective score (M-GSS) were used for outcomes.
A total of 46,280 SMI images were extracted from 166 esophageal ESD videos in 4 hospitals between 2016 and 2024. For marks detection, the precision of the AI model in the training dataset, internal, and external test datasets were 78.97%, 85.76%, and 79.46%. For the incision guide line, the average distance error (ADE) was 0.096, 0.097, and 0.159, respectively. The AI model achieved an accuracy (63.21% vs 54.57%, P < 0.001), ADE (0.081 vs 0.241, P = 0.002), and M-GSS (1.88 vs 1.75, P < 0.001) significantly better than that of junior endoscopists, and were comparable with those of senior endoscopists.
The AI model achieved promising results on large SMI image datasets, showing comparable effectiveness with senior endoscopists for marks detection and incision guide line, which had the potential to navigate SMI safely and properly.
内镜黏膜下剥离术（ESD）是治疗早期食管癌的一项重要微创手术，其中周围黏膜切口（SMI）是关键且具有挑战性的阶段。本研究旨在开发一种人工智能（AI）模型，用于在SMI期间进行标记检测和切口指导线预测，并与多中心内镜医师比较其性能。
从视频片段中每秒提取一帧图像，并分为3个数据集：训练数据集、内部和外部测试数据集。22名内镜医师参与了与该模型的比较，其中包括8名高级内镜医师和14名初级内镜医师。使用多个客观指标和标记指导线主观评分（M-GSS）作为结果。
2016年至2024年间，从4家医院的166例食管ESD视频中总共提取了46280张SMI图像。对于标记检测，AI模型在训练数据集、内部和外部测试数据集中的精度分别为78.97%、85.76%和79.46%。对于切口指导线，平均距离误差（ADE）分别为0.096、0.097和0.159。AI模型在准确性（63.21%对54.57%，P<0.001）、ADE（0.081对0.241，P=0.002）和M-GSS（1.88对1.75，P<0.001）方面显著优于初级内镜医师，与高级内镜医师相当。
AI模型在大型SMI图像数据集上取得了有前景的结果，在标记检测和切口指导线方面显示出与高级内镜医师相当的有效性，有潜力安全、正确地指导SMI。
