一个深度学习模型,从12导电心电图中推断出高的肺毛细血管压
Daphne E Schlesinger1,2,3, Nathaniel Diamant4, Aniruddh Raghu3,5
1Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA.
JACC. Advances
|June 28, 2024
概括
一种新的深度学习模型可以从心电图 (ECG) 中非侵入性地推断出高平均肺毛细血管压 (mPCWP). 当侵入性血液动力学监测是不可行的时,这种方法提供了一个潜在的替代方案,改善了临床决策.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 中心血动力学参数通常通过肺动脉导管测量侵入性地测量,这带有风险,并且不是普遍可用的.
- 升高的平均肺毛细血管压 (mPCWP) 是各种心血管疾病的关键指标.
研究的目的:
- 开发一种非侵入性的方法来识别使用12导电心电图 (ECG) 的升高mPCWP.
- 利用深度学习从心电图数据中推断mPCWP,为侵入性测量提供潜在的替代方案.
主要方法:
- 通过使用来自马萨诸塞州综合医院的248,955份临床记录,开发了一种深度学习模型.
- 该模型经过训练,从ECG中推断出mPCWP>15 mmHg,其中的一部分数据用于预训练和直接mPCWP测量,用于模型开发和验证.
- 开发了一个不可靠性得分,以量化模型预测的可靠性.
主要成果:
- 该模型在测试组件上实现了接收器操作特征曲线 (AUC) 下的面积为0.80 ± 0.02,在持有组件上为0.79 ± 0.01.
- 模型的性能取决于不可靠性得分,较高的得分表明性能较差 (例如,AUC在最高分位数中为0.70±0.06).
结论:
- 通过使用深度学习,可以从心电图中非侵入性地推断出平均肺毛细血管压 (mPCWP).
- 这些推断的可靠性可以量化,提供有价值的临床信息,当侵入性监测是不容易获得或可行的.
相关概念视频
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