Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
University of Kansas School of Medicine and VA Medical Center, Kansas City, MO, USA.
Lancet Gastroenterol Hepatol. 2021 Sep;6(9):700-708. doi: 10.1016/S2468-1253(21)00216-8. Epub 2021 Jul 21.
White light endoscopy is a pivotal first-line tool for the detection of gastric neoplasms. However, gastric neoplasms can be missed during upper gastrointestinal endoscopy due to the subtle nature of these lesions and varying skill among endoscopists. Here, we aimed to evaluate the effect of an artificial intelligence (AI) system designed to detect focal lesions and diagnose gastric neoplasms on reducing the miss rate of gastric neoplasms in clinical practice.
This single-centre, randomised controlled, tandem trial was done at Renmin Hospital of Wuhan University, China. We recruited consecutive patients (≥18 years old) undergoing routine upper gastrointestinal endoscopy for screening, surveillance, or investigation of symptoms. Same-day tandem upper gastrointestinal endoscopy was done where patients first underwent either AI-assisted (AI-first) or routine (routine-first) white light endoscopy, followed immediately by the other procedure, with targeted biopsies for all detected lesions taken at the end of the second examination. Patients were randomly assigned (1:1) to the AI-first or routine-first group using a computer-generated random numerical series and block randomisation (block size of four). Endoscopists were not blinded to randomisation status, whereas patients and pathologists were. The primary endpoint was the miss rate of gastric neoplasms and the analysis was done per protocol. This trial is registered with the Chinese Clinical Trial Registry, ChiCTR2000034453, and has been completed.
Between July 6, 2020, and Dec 11, 2020, 907 patients were randomly assigned to the AI-first group and 905 to the routine-first group. The gastric neoplasm miss rate was significantly lower in the AI-first group than in the routine-first group (6·1%, 95% CI 1·6-17·9 [3/49] vs 27·3%, 15·5-43·0 [12/44]; relative risk 0·224, 95% CI 0·068-0·744; p=0·015). The only reported adverse event was bleeding from a target lesion after biopsy.
The use of an AI system during upper gastrointestinal endoscopy significantly reduced the gastric neoplasm miss rate. AI-assisted endoscopy has the potential to improve the yield of gastric neoplasms by endoscopists.
The Project of Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision and the Hubei Province Major Science and Technology Innovation Project.
白光内镜是检测胃肿瘤的一线重要工具。然而，由于这些病变的细微性质和内镜医生技能的差异，在上消化道内镜检查中可能会遗漏胃肿瘤。在此，我们旨在评估旨在检测局灶性病变和诊断胃肿瘤的人工智能 (AI) 系统在降低临床实践中胃肿瘤漏诊率方面的效果。
这是一项在中国武汉大学人民医院进行的单中心、随机对照、串联试验。我们招募了接受常规上消化道内镜检查进行筛查、监测或症状调查的连续患者（≥18 岁）。同一天进行串联上消化道内镜检查，患者首先接受 AI 辅助（AI 优先）或常规（常规优先）白光内镜检查，然后立即进行另一项检查，在第二次检查结束时对所有检测到的病变进行靶向活检。患者使用计算机生成的随机数字序列和区组随机化（区组大小为 4）按 1:1 随机分配到 AI 优先或常规优先组。内镜医生对随机分组状态不设盲，而患者和病理学家设盲。主要终点是胃肿瘤的漏诊率，且按方案进行分析。该试验在中国临床试验注册中心注册，注册号 ChiCTR2000034453，现已完成。
在 2020 年 7 月 6 日至 2020 年 12 月 11 日期间，907 名患者被随机分配到 AI 优先组，905 名患者被随机分配到常规优先组。AI 优先组的胃肿瘤漏诊率明显低于常规优先组（6.1%，95%CI 1.6-17.9[3/49]vs27.3%，15.5-43.0[12/44]；相对风险 0.224，95%CI 0.068-0.744；p=0.015）。唯一报告的不良事件是活检后目标病变出血。
在上消化道内镜检查期间使用 AI 系统可显著降低胃肿瘤的漏诊率。AI 辅助内镜检查有可能通过内镜医生提高胃肿瘤的检出率。
湖北省消化病微创切口临床研究中心项目和湖北省重大科技创新项目。
