负小肠囊内镜的深度学习改善了小肠病变的检测和诊断产量
Kyung Seok Choi1, DoGyeom Park2, Jin Su Kim1
1Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
概括
一个深层卷积神经网络 (CNN) 模型在人类审查中错过的负小肠囊内镜 (SBCE) 视频中发现了重要的发现. 这种人工智能工具有望提高胃肠道出血病例的诊断准确性.
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) 对于诊断不清楚的胃肠道出血至关重要.
- 以前的研究强调了AI在检测SBCE异常方面的潜力,但临床实用性仍未得到充分探索.
- 人类对SBCE视频的解释可能耗时且容易出现错误,可能会错过微妙的发现.
研究的目的:
- 评估深度卷积神经网络 (CNN) 模型在重新分析负SBCE视频中的临床有用性.
- 为了确定SBCE视频中是否可以检测到有意义的发现,例如血管切开和,这些视频最初被人类读者认为是负面的.
- 评估CNN辅助再分析对患者诊断和随后临床结果的影响.
主要方法:
- 在两个学术医院,从怀疑小肠出血的患者收集SBCE视频的回顾性收集.
- 使用先前开发的CNN算法,重新分析最初被归类为负面的SBCE视频.
- 两位胃肠科医生对CNN识别的图像进行了独立审查,以确认诸如血管切开和等有意义的发现.
主要成果:
- 在202个SBCE视频中,103个 (51.0%) 最初是负面的. 在这些负面视频中,CNN模型在63个 (61.2%) 中检测到有意义的发现.
- 有意义的发现包括40个视频中的79个血管内和35个视频中的66个.
- 重新分析导致了10.3%的患者的诊断变化,在有或没有CNN检测到的发现的组之间,再出血率没有统计学上显著的差异.
结论:
- 开发的CNN算法成功地在SBCE视频中识别出了在初始人类解释过程中被忽视的临床相关发现.
- 深度CNN对SBCE图像的分析可以成为增强人类审查和潜在减少诊断错误的宝贵工具.
- 这种人工智能驱动的方法有望提高SBCE的诊断产量,特别是在怀疑小肠出血的情况下.
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