声心图视频驱动的多任务学习模型用于冠状动脉疾病诊断和严重程度分级
Ying Guo1, Yu-Han Cai2, Tao Xu1
1Department of Cardiology, Beijing Hospital, National Center for Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Frontiers in bioengineering and biotechnology
|August 11, 2025
概括
一个人工智能模型,用于CAD的智能回声 (IE-CAD),有效地使用回声心脏学检查和评分冠状动脉疾病 (CAD). 这种工具有助于更快地诊断和评估患者的CAD严重程度.
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:
- 心声图是诊断冠状动脉疾病 (CAD) 的主要非侵入性方法.
- 目前专家进行的视觉评估需要大量的时间,这凸显了对自动化解决方案的需求.
研究的目的:
- 开发和验证人工智能模型,用于CAD (IE-CAD) 的智能回声,用于自动CAD查和狭窄分级.
- 提高CAD诊断的效率和准确性,使用心声回声视频分析.
主要方法:
- 使用3DdeeplabV3+骨干构建了一个声心视频驱动的多任务学习模型 (IE-CAD).
- 集成多任务学习以捕捉动态心肌轮并提取多种特征.
- 在北京医院的870个心声回声录像上训练和测试该模型,并在福威医院的450个视频上验证.
主要成果:
- 对于检测显著/严重的CAD,IE-CAD模型的AUC为0.78和灵敏度为0.85.
- 证明了0.545的皮尔森相关系数,用于预测甘西尼得分.
- 在外部验证数据集上获得了0.77的AUC和0.78的灵敏度.
结论:
- IE-CAD模型显示了有效的CAD诊断和临床怀疑患者的分级的巨大潜力.
- 这种人工智能驱动的方法可以增强冠状动脉疾病的诊断工作流程.
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