人工智能在现实世界中的应用,用于检测病理性胃缺陷和新生病变
Young Hoon Chang1, Cheol Min Shin2, Hae Dong Lee1
1Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Journal of gastric cancer
|July 3, 2024
概括
与初始活检相比,人工智能系统ENAD CAD-G改善了胃病变的诊断. 这种人工智能工具可以帮助内镜医生识别需要切除的病变.
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:
- 胃病变的初始内镜活检结果往往不同于最终的病理诊断.
- 差异可能会影响治疗决策和患者的治疗结果.
研究的目的:
- 评估基于人工智能的系统ENdoscopy作为人工智能驱动的设备计算机辅助胃镜诊断 (ENAD CAD-G) 的有效性,以减少胃病变的诊断差异.
- 评估ENAD CAD-G在检测早期胃癌 (EGC),发育不良和良性病变方面的表现.
主要方法:
- 从9892名患者 (2011-2021) 收集了24948张内镜图像的回顾性收集.
- 在三个现实数据集上对ENAD CAD-G的评估:社区诊所转诊与异型 (n=154),切除的内部视频集 (n=140),查/怀疑的外部视频集 (n=296).
主要成果:
- ENAD CAD-G在分类参考病变方面取得了高准确性:EGC (82.47%),发育不良 (88.31%) 和良性 (83.12%).
- 在内部视频组中,ENAD CAD-G显示出与初始活检 (60.71%,P<0.001) 相比,对于发育不良 (88.57%) 和EGC (91.43%) 的精度更高.
- 外部视频集显示了EGC的准确率为87.50%,形症的准确率为90.54%,良性病变的准确率为88.85%.
结论:
- 在检测和诊断需要内镜切除的胃病变方面,ENAD CAD-G显著优于初始活检.
- 人工智能系统可以有效地帮助社区内镜医生识别关键的胃病变.
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