深度学习以诊断心回:一个外部验证研究.
Ido Cohen1,2, Jeffrey G Malins3, Michal Cohen-Shelly1,4,5
1Leviev Cardiovascular Institute, Sheba Medical Center, Derech Sheba 2, Tel HaShomer, Ramat Gan 52621, Israel.
European heart journal. Digital health
|September 23, 2025
概括
人工智能 (AI) 在诊断心声图中的 mitra 和 tricuspid 吐时显示出高准确度,这可能会改善对心脏诊断的准入. 对于现实世界的应用,需要进一步验证.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 在老年人中, mitra 和 tricuspid 反是普遍存在的,导致显著的发病率和死亡率.
- 胸外心声扫描 (TTE) 是诊断的标准,但其可访问性有限.
- 基于人工智能的回声心电图分析提供了一种潜在的解决方案,可以弥合这一诊断差距.
研究的目的:
- 为了外部验证一个深度学习算法来分类心房反的严重程度.
- 用TTE研究来评估算法的性能与心脏病学家的解释.
主要方法:
- 一个深度学习算法使用来自梅奥诊所卫生系统的TTE研究得到了验证.
- 该模型分析了心声回声图像,以分类二手腹 (MR) 和三腹 (TR) 的严重程度.
- 使用二进制和顺序分类方案评估性能,其中包括接收器运行特征曲线 (AUC) 下的面积,准确性,灵敏性和特异性.
主要成果:
- 人工智能模型在MR (AUC 0.98,95%CI:0.97-0.99) 和TR (AUC 0.96,95%CI:0.94-0.98) 两方面都实现了高性能.
- 具体的性能指标包括91%的精度,95%的灵敏度和89%的特异性对MR,以及84%的精度,91%的灵敏度和80%的特异性对TR.
- 该模型在1541项符合条件的TTE研究中产生了578项预测.
结论:
- 人工智能模型在产生预测时,在识别临床上显著的心房反方面表现出高的诊断性能.
- 研究结果支持在不同患者群体中使用人工智能辅助心声回声学的可行性.
- 人工智能模型要求与本地心声回声采集实践之间的技术协调对于现实世界的应用性至关重要.
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