基于心电图的人工智能识别冠状动脉疾病
Shinwan Kany1, Samuel F Friedman2, Mostafa Al-Alusi3
1Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Cardiology, University Heart and Vascular Center Hamburg-Eppendorf, Hamburg, Germany.
一个新的深度学习模型,ECG2CAD,可以使用心电图 (ECG) 检测冠状动脉疾病 (CAD),并识别高风险个体. 这种人工智能工具在初级环境中对早期检测和预防性护理充满希望.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 冠状动脉疾病 (CAD) 是导致疾病和死亡的主要原因.
- 早期发现CAD对于有效管理和预防不良结果至关重要.
研究的目的:
- 开发一个深度学习模型 (ECG2CAD) 用于使用心电图 (ECG) 检测CAD.
- 通过使用心电图数据,在初级保健环境中识别有不良事件风险的个体.
主要方法:
- 在马萨诸塞州总医院 (MGH) 接受了超过76万张心电图的ECG2CAD培训.
- 在MGH,布里格姆和妇女医院 (BWH) 和英国生物库数据集中使用AUROC和AUPRC评估模型性能.
- 在BWH初级保健队列中评估了与CAD相关事件的风险.
主要成果:
- 在所有测试组中,ECG2CAD在区分流行CAD方面表现一致.
- 该模型显示了相对于传统风险模型 (年龄,性别,聚合队列方程) 的增量值.
- 高的ECG2CAD风险得分与心肌梗塞,心力衰竭和全因死亡率显著增加的风险相关.
结论:
- 人工智能驱动的心电图分析可以帮助识别潜在的未被诊断的CAD患者.
- 这项技术可以为进一步的诊断测试和CAD的预防策略提供信息.
- 在初级保健机构,ECG2CAD为风险分层提供了一个有前途的工具.
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