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基于深度学习的系统对小肠囊内镜读数的临床实用性的比较

Tomonori Aoki^1,2, Atsuo Yamada¹, Shiro Oka³

¹Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.

Journal of gastroenterology and hepatology

|October 13, 2023

概括

一个新的卷积神经网络 (CNN) 系统通过减少阅读时间和压力来改善小肠囊内镜 (SBCE) 分析. 这种人工智能工具保持了内镜师高异常检测率.

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:

小肠囊内镜 (SBCE) 可以产生大量的图像数据.
在SBCE中使用卷积神经网络 (CNN) 进行自动异常检测是一个新兴领域.
不同的CNN系统的直接临床实用性比较缺乏.

研究的目的:

为了比较现有的CNN系统与分析SBCE图像的新型CNN系统的临床实用性.
评估CNN查对内镜医生阅读时间和心理压力的影响.
评估内镜师在有或没有CNN协助的情况下的诊断性能.

主要方法:

追溯分析36个完整的SBCE视频,其中43个确认异常病变.
三种阅读过程的比较: (A) 没有CNN, (B) 现有的CNN选, (C) 新的CNN选.
实习内镜师在每个过程中评估视频,记录阅读时间和压力水平.

主要成果:

新的CNN系统捕获了9.4%的图像,而现有的系统捕获了24.3%.
在所有组中,异常检测率相似 (A:77%,B:67%,C:79%).
与其他方法相比,新型CNN系统显著减少了平均阅读时间 (10.1分钟) 和心理压力 (1.8).

结论:

新的CNN系统通过减少内镜师的工作量而不会影响检测准确度,从而提高了SBCE的临床效用.
CNN系统的性能是影响其临床适用性的关键因素.
在临床实践中广泛采用CNN系统之前,必须仔细评估CNN系统.

关键词:

人工智能的人工智能是人工智能.囊内镜检查囊内镜检查卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.心理上的压力是心理上的压力.

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