多式人工智能可以预测过度缩性心肌病症中心律不整的死亡
Changxin Lai1,2, Minglang Yin1,2, Eugene G Kholmovski1,2
1Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.
Nature cardiovascular research
|July 2, 2025
概括
一个新的深度学习工具MAARS,使用多式数据准确地预测高性心肌病患者的致命心室节律失常. 这种人工智能方法超越了当前的指导方针,并确保了不同患者群体的公平性.
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:
- 由于心室节律失常引起的突然心脏死亡是全球主要的健康问题.
- 预测高伤心肌病 (HCM) 患者的心律失常死亡是很困难的,目前的指导方针显示的准确性有限.
- 对HCM患者现有的风险分层方法需要改进.
研究的目的:
- 开发和验证一个深度学习模型,MAARS (心室失常风险分层的多模式人工智能),用于预测HCM中致命的心室失常.
- 提高心脏病突发死亡风险的HCM患者风险分层的准确性和公平性.
主要方法:
- MAARS使用基于变压器的神经网络来分析多模式数据,包括电子健康记录,心声图和放射学报告以及对比增强的心脏磁共振成像.
- 该模型在内部和外部患者队列上进行了训练和验证.
主要成果:
- 在内部队列中,MAARS在曲线下的面积 (AUC) 为0.89,在外部队列中为0.81.
- 该模型显著优于当前的临床指南,AUC的改善为0.27-0.35 (内部) 和0.22-0.30 (外部).
- 与目前的指导方针不同,MAARS证明了人口学子组之间的公平性.
结论:
- MAARS代表了一种新且有效的深度学习方法,用于预测HCM患者的致命腹腔失常.
- 该模型能够整合多式联络数据,包括心脏MRI,提高预测准确度.
- 马尔斯为HCM风险分层提供了一个更准确,更公平,更透明的工具,有可能改善患者的治疗结果.
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