在白光内镜期间通过卷积神经网络实时评估Helicobacter pylori感染:一个前性的多中心研究 (带视频)
Yuqin Shen1,2, Angli Chen3, Xinsen Zhang4
1Department of Gastroenterology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou, China.
Clinical and translational gastroenterology
|October 6, 2023
概括
一个新的AI系统,CADSS-HP,在没有活检的情况下,在内镜检查期间准确检测Helicobacter pylori感染. 它显示出比传统方法更高的灵敏度,有助于更快的诊断.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 杆菌感染的诊断通常依赖于侵入性胃活检.
- 像尿素呼吸测试这样的非侵入性方法也有局限性.
- 内镜评估为实时诊断提供了一个潜在的窗口.
研究的目的:
- 用卷积神经网络评估用于H. pylori感染的计算机辅助决策支持系统 (CADSS-HP) 的诊断准确性.
- 为了比较CADSS-HP的性能与内镜印记,尿素呼吸试验 (URT) 和组织病理学.
- 评估AI在内镜检查过程中非侵入性检测H. pylori的潜力.
主要方法:
- 使用599名患者的白光内镜视频进行CADSS-HP的开发.
- 在3个中心对456名患者进行CADSS-HP的前性评估.
- 将CADSS-HP准确度与内镜诊断,URT和组织病理学进行比较,通过组织病理学和/或URT定义的H. pylori感染.
主要成果:
- 在CADSS-HP中,AUC达到0.95,敏感度为91.5%,特异性为88.8%.
- 该系统的灵敏度和准确性高于内镜医生的内镜诊断.
- CADSS-HP的灵敏度与URT相比,优于组织病理学.
结论:
- 在实时内镜检查中,CADSS-HP对诊断H. pylori感染具有很高的灵敏度.
- 人工智能系统的性能优于直接内镜诊断,可与URT相比.
- CADSS-HP为H. pylori检测提供了一个有前途的非侵入性工具.
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