人工智能用于单图像诊断自身免疫性胃炎使用胃体缩图像:一个多中心的回顾性诊断研究
Yiming Song1, Xunbing Zhang2, Liwei Shi3
1Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, NHC Key Laboratory of Digestive Diseases, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
这项研究开发了一种人工智能系统,用于使用内镜图像诊断自身免疫性胃炎 (AIG). 人工智能实现了专家级准确性,在区分AIG和H. pylori胃炎方面超过了新手和高级内镜师.
科学领域:
- 胃肠病学 胃肠病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 通过内镜将自身免疫性胃炎 (AIG) 与与H. pylori相关的缩性胃炎 (HpAG) 区分开来是具有挑战性的.
- 错误诊断可能导致不适当的患者治疗和管理.
- 需要新的诊断工具来提高准确性.
研究的目的:
- 开发和验证用于诊断AIG的人工智能 (AI) 系统.
- 人工智能系统使用单个白光内镜图像来显示胃体缩.
- 将AI的诊断性能与人类内镜师进行比较.
主要方法:
- 一个多中心的回顾性研究,涉及2137名患者 (1064名AIG,1073名HpAG).
- 在两个中心的数据上使用RegNet架构开发CorpusNet-AIG.
- 外部验证对来自三个额外中心的数据进行了验证,将AI与15名内镜师进行了比较.
主要成果:
- 在内部验证 (准确率94.51%,AUC 0.990) 和外部验证 (准确率92.94%,AUC 0.972) 中,CorpusNet-AIG表现出很高的性能.
- 人工智能系统使用单个图像显著优于新手和高级内镜师.
- 人工智能性能与专家内镜师相美,甚至比新手和老年人优越,即使具有完整的图像集.
结论:
- 开发了第一个能够从单个内镜图像进行专家级AIG诊断的AI系统.
- 人工智能系统显示了改善AIG诊断的巨大潜力.
- 建议对临床实施进行进一步的前性多中心验证.
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