通过深度学习对右心室功能的评估,改善了对腹回的跨导管边缘到边缘修复后的预后
Mark Lachmann1,2, Vera Fortmeier3, Lukas Stolz4
1First Department of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany (M.L., A.H., E.R., J.T., K.-L.L.).
Circulation. Cardiovascular imaging
|January 21, 2025
概括
深度学习可以准确地从心声图中预测右心室喷射率 (RVEF),改善经过过导管端到端修复 (TEER) 的患者的预后. 这种人工智能驱动的RVEF评估优于预测TEER后死亡风险的传统方法.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 心声回声扫描 (Echocardiography) 是一种心声回声扫描.
背景情况:
- 右心室 (RV) 功能是经过跨导管边缘到边缘修复 (TEER) 的严重腹 (MR) 患者的关键预后指标.
- 三环状平面缩外流 (TAPSE) 是 RV 功能的常规回声心脏学测量.
- 一种新的深度学习 (DL) 模型可以高准确度地从二维心声录像中预测RV喷射率 (RVEF).
研究的目的:
- 为了评估DL预测的RVEF的预后值在接受TEER的重度MR患者中.
- 为了比较DL-RVEF与TAPSE对1年死亡率的预测性能.
主要方法:
- 一项多中心注册研究分析了1154名接受TEER的重症MR患者.
- 程序前的2D胸前回声心脏成像视频使用经过验证的DL模型进行处理,以预测RVEF.
- 评估了预测的RVEF和1年死亡率之间的关联.
主要成果:
- 根据DL预测的RVEF与TAPSE的相关性很小 (R=0.33).
- 在预测1年死亡率方面,DL预测的RVEF优于TAPSE (AUC0.687对0.625,P=0.029).
- 预测RVEF降低的患者 (<45%) 与保留RVEF (92.1%;HR 2.67,P<0.001) 患者相比,他们的1年生存率 (80.3%) 显著降低.
结论:
- 启用DL的RVEF评估可以在接受TEER的重症MR患者的预后改进.
- 这种人工智能方法可以识别RV功能障碍的患者,他们可能会从更密切的监测中受益.
- DL预测的RVEF为这种患者群体的风险分层提供了有价值的工具.
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