一个深度学习模型,从心声图中识别心交膜缩
Mostafa A Al-Alusi1, Emily S Lau2, Aeron M Small3
1Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Harvard Medical School, Boston, Massachusetts, USA; Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts, USA.
JACC. Cardiovascular imaging
|October 1, 2025
概括
一个新的深度学习模型,DROID-MVP,从心声图视频中准确地检测到对心脱落 (MVP). 它的预测与头回的严重程度和未来的门手术相关,可能会自动诊断.
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:
- 密特拉脱落 (MVP) 影响2-3%的人口,增加心力衰竭和突然死亡的风险.
- 传统的通过胸前回声心脏成像进行诊断是耗时的,需要专门的专业知识.
研究的目的:
- 开发和验证一个深度学习模型,DROID-MVP,用于从数字心声回声图视频中自动化MVP分类.
- 评估DROID-MVP预测与临床结果 (如额头回和额头门修复/更换) 之间的关联.
主要方法:
- 经过培训和验证,DROID-MVP已经在16902名心脏病患者的100多万个心声回声录像视频上进行了验证.
- 在两个主要医院 (MGH和BWH) 的初级保健患者队列上对该模型进行了外部验证.
- 评估了DROID-MVP分数,额头膜反严重程度和随后的额头膜手术之间的相关性.
主要成果:
- 在内部和外部验证集中,DROID-MVP在识别MVP方面表现出高准确度 (AUROC范围从0.947到0.968).
- 较高的DROID-MVP得分与中度至严重的 mitra regurgitation (OR: 2.0) 和未来的 mitra 门修复或更换 (HR: 3.7) 有显著的关联.
结论:
- 深度学习模型DROID-MVP有效地从心声回声图视频中识别MVP.
- 模型预测作为与临床显著MVP相关的数字标记,可能有助于诊断和风险分层.
- 这项技术可以自动化MVP诊断并改善患者管理.
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