Li Bing, Dong Yue-Lun, Liu Jing-Yi, Tan Wei-Min, He Dong-Li, Qi Zhi-Peng, Yu Hon-Ho, Shi Qiang, Ren Zhong, Cai Ming-Yan, Cai Shi-Lun, Yan Bo, Zhong Yun-Shi
Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai, China.
School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China.
Int J Surg. 2025 Jun 20. doi: 10.1097/JS9.0000000000002748.
Multiple artificial intelligence (AI) systems have been developed to assist with endoscopic diagnosis. We established the first real-time AI lesion-labeling system to assist in delineating lesion margin during endoscopic submucosal dissection (ESD). We aimed to further validate the efficacy of this system in improving histological complete resection rate especially for beginners in low-volume centers.
We performed this prospective cohort study in two endoscopy centers in Shanghai, China from January 2021 to December 2022. Eligible patients in a low-volume center equipped with real-time AI lesion labeling system were recruited consecutively to the AI group and underwent ESD performed mainly by beginners, while participants in a high-volume center underwent conventional ESD performed by experienced endoscopists. The primary outcome was complete lateral resection rate according to pathological examination of the specimen resected. Secondary outcomes were en -bloc resection rate, procedure duration, specimen diameter and complication rate.
174 patients (200 lesions) were recruited into the AI-assisted ESD group. With the use of lesion-margin labeling system, 90.0% (180/200) of ESD cases achieved negative lateral margins. The en bloc resection rate was 98.5% (197/200), and the histological complete resection rate was 87.5% (175/200). 181 patients (202 lesions) received conventional ESD in a high-volume center. There was no significant difference between AI-assisted and conventional ESD group regarding the rate of complete lateral resection (90.0% [180/200] vs 92.1% [186/202], P = 0.465). Total procedure duration (min) was significantly longer in AI-assisted group (82 [54,106]) compared with conventional group (49 [30,63], P < 0.001).
AI lesion-labeling system showed reliable safety and efficacy in assisting to identify the margin of lesions before ESD, and potential to improve complete lateral resection rate of ESD for esophageal lesion in low-volume endoscopy centers.
已开发出多种人工智能(AI)系统辅助内镜诊断。我们建立了首个实时AI病变标记系统,以在内镜黏膜下剥离术(ESD)期间协助勾勒病变边缘。我们旨在进一步验证该系统在提高组织学完全切除率方面的有效性,尤其是对于低容量中心的初学者。
2021年1月至2022年12月,我们在中国上海的两个内镜中心进行了这项前瞻性队列研究。配备实时AI病变标记系统的低容量中心的符合条件的患者连续被纳入AI组,并主要由初学者进行ESD,而高容量中心的参与者则接受由经验丰富的内镜医师进行的传统ESD。主要结局是根据切除标本的病理检查得出的完全侧切率。次要结局是整块切除率、手术持续时间、标本直径和并发症发生率。
174例患者(200个病变)被纳入AI辅助ESD组。使用病变边缘标记系统,90.0%(180/200)的ESD病例侧切缘阴性。整块切除率为98.5%(197/200),组织学完全切除率为87.5%(175/200)。181例患者(202个病变)在高容量中心接受了传统ESD。AI辅助组和传统ESD组在完全侧切率方面无显著差异(90.0% [180/200] 对92.1% [186/202],P = 0.465)。AI辅助组的总手术持续时间(分钟)明显长于传统组(82 [54,106])对(49 [30,63],P < 0.001)。
AI病变标记系统在ESD前协助识别病变边缘方面显示出可靠的安全性和有效性,并且有可能提高低容量内镜中心食管病变ESD的完全侧切率。