基于人工智能的诊断标准内镜超声波扫描 Biliopancreatic 系统的扫描站点:一个多中心的回顾性研究
Shuxin Tian1,2,3, Huiying Shi1, Weigang Chen2,3
1Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan.
International journal of surgery (London, England)
|December 11, 2023
概括
一个新的卷积神经网络 (CNN) 模型准确地识别了内镜超声波 (EUS) 图像上的胆腺系统解剖学. 这个人工智能工具表现出高性能,帮助初学者在EUS图像解释.
科学领域:
- 医学成像分析 医学成像分析
- 人工智能在胃肠病学中的应用
- 胆腺系统解剖学 胆腺系统解剖学
背景情况:
- 初学者面临的挑战是,在内镜超声波 (EUS) 图像上识别标准胆腺系统解剖部位.
- 准确的解剖部位识别对于正确的诊断和治疗规划在EUS程序中至关重要.
研究的目的:
- 开发和评估基于卷积神经网络 (CNN) 的模型,用于在EUS图像上自动识别标准胆板系统解剖部位.
- 将CNN模型的诊断性能与人类初学者和专家进行比较.
主要方法:
- 通过使用来自1812名患者的6230张EUS图像来训练CNN模型,覆盖了14个标准解剖部位.
- 内部验证涉及47名患者的1569张图像;外部验证使用16个中心131名患者的85,322张图像.
- 通过比较CNN的模型,初学者和专家的解释,以专家的决定作为黄金标准来评估性能.
主要成果:
- 在内部测试中,CNN模型获得了高准确度 (92.1-100.0%),在外部验证中获得了优异的灵敏度 (89.45-99.92%) 和特异性 (93.35-99.79%).
- 与初学者相比,CNN模型在14个站点中的11个站点显示出更高的灵敏度和特异性.
- 该模型与专家解释有很强的一致性,由高卡帕值 (0.84-0.98) 表示.
结论:
- 基于CNN的模型可以自动识别EUS图像上的标准解剖部位,具有出色的诊断性能.
- 这种人工智能工具有可能成为临床医生有价值的辅助工具,特别是在提高初学者解释能力方面.
- 进一步整合这些人工智能工具可以提高EUS检查在临床实践中的效率和准确性.
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