人工智能支持对右心室功能进行心声评估.
medRxiv : the preprint server for health sciences
|February 6, 2026
概括
一个新的深度学习模型,EchoNet-RV,使用心声图准确评估右心室 (RV) 功能. 该工具提供了快速的,自动化的RV微分面积变化 (RVFAC) 估计,超越了人类的变异性,有助于疾病监测.
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:
- 右心室 (RV) 功能对心血管健康至关重要,但由于复杂的解剖学和位置,通过心声扫描进行评估是具有挑战性的.
- 观察者之间的有限可重现性阻碍了准确的RV功能评估.
研究的目的:
- 开发EchoNet-RV,这是一个新的深度学习模型,用于自动化RV细分和RV微分面积变化 (RVFAC) 从心声视频的估计.
- 对不同数据集的专家注释和现有模型进行EchoNet-RV性能评估.
主要方法:
- 在7169个专家注释的角四室视图 (A4C) 声心录像视频上训练了EchoNet-RV.
- 在内部 (1,320个视频) 和两个国际外部测试集 (3,107个和1,077个视频) 上验证了模型.
- 评估预测的RVFAC与临床结果 (心力衰竭住院治疗或全因死亡) 之间的关联.
主要成果:
- EchoNet-RV实现了 RV 分段的高子系数 (0.788-0.893) 和 RVFAC 估计的低平均绝对误差 (5.795-6.362 个百分点).
- 预测误差明显低于观察者之间的变化 (p<0.001).
- 该模型在识别RV功能障碍 (AUC 0.684-0.859) 中表现强,并且表现优于两个多任务模型.
结论:
- EchoNet-RV提供快速,自动化的RVFAC评估,其性能超过了观察者之间的变化.
- 该模型显示出在临床实践中作为VR功能评估和疾病监测的有价值工具的巨大潜力.
- 预测的RVFAC值与不良临床结果相反相关,突出了预后价值.
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