使用多视图深度学习进行综合性大动脉狭窄的表征
Hirotaka Ieki1,2,3, Yuki Sahashi4, Miloš Vukadinovic3,5
1Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA.
medRxiv : the preprint server for health sciences
|November 19, 2025
概括
EchoNet-AS整合了心声回声图的结构和功能数据,以准确评估大动脉狭窄 (AS) 的严重程度. 这种人工智能工具在各种数据集中表现出强的表现,为临床决策支持系统提供了潜力.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 准确的大动脉狭窄 (AS) 评估需要整合结构和功能信息.
- 当前的人工智能 (AI) 模型通常只使用结构或功能数据.
研究的目的:
- 开发EchoNet-AS,用于全面评估AS严重程度的综合AI方法.
- 结合卷积神经网络用于门运动分析和分段模型用于多普勒测量.
主要方法:
- 开发了EchoNet-AS,这是一个开源的,端到端的AI模型.
- 利用卷积神经网络进行视频分析和对多普勒测量进行细分模型.
- 在超过21万张图像上进行训练,并在多个大型,多样化的队伍中进行验证.
主要成果:
- 在AS严重程度的分类中,EchoNet-AS实现了高精度,在内部和外部验证队列中,AUC高达0.989.
- 综合方法的表现优于使用单个数据类型 (视频或多普勒测量) 的模型.
- 在多个医疗保健系统中展示了强大的性能和通用性.
结论:
- EchoNet-AS有效地合成了B模式和多普勒回声心脏学数据,以进行准确的AS评估.
- 人工智能模型显示出作为AS的自动化临床决策支持工具的巨大潜力.
- 该方法向外部验证队伍展示了强大的概括性.
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