在肺动脉高血压中以人工智能为基础的心声学
Bettia Celestin1, Shadi P Bagherzadeh2, Everton Santana2
1Stanford University, Division of Cardiovascular Medicine, Department of Medicine, Palo Alto, CA; Stanford Cardiovascular Institute, Palo Alto, CA; Stanford Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Stanford University, Palo Alto, CA.
Chest
|August 28, 2025
概括
对心声回声学的深度学习 (DL) 工作流程在评估肺高血压 (PH) 中具有前景,可能提高临床效率. 虽然大多数测量是准确的,但需要进一步调查RV部分面积变化 (RVFAC).
科学领域:
- 心脏病学
- 人工智能
- 医学成像
背景情况:
- 对肺高血压 (PH) 的评估,心声学是至关重要的.
- 手动解释是耗时且容易出现错误的.
- 需要自动化解决方案来提高效率和准确性.
研究的目的:
- 评估PH评估的心声回应全自动化深度学习 (DL) 工作流程的可靠性.
- 与实验室核心读数相比,确定DL解释的偏差和精度.
- 评估DL能够辨别较轻微的PH形式.
主要方法:
- 两个研究部分:偏差/精度评估和较轻的PH的区分.
- 使用Us2.ai软件1.4.5版本,使用核心实验室 (CL) 读取器作为参考.
- 包括病例对照和转诊队列,这些队列的参数包括三回速度 (TRV) 和右心室 (RV) 功能.
主要成果:
- 对于峰值TRV,RA区域和TAPSE没有显著偏差;RVFAC显示显著偏差 (11. 46%).
- 大多数测量的精度低于15%.
- 检测PH的曲线下的面积 (AUC) 在一个队列中为DL0. 98,而在转诊队列中为CL0. 80.
结论:
- 自动化DL工作流程对心声学中的PH评估具有前景.
- 它有可能提高临床实践的效率.
- 可能需要进一步验证,特别是RVFAC.
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