使用人工智能在心电图上检测高伤心肌病的检测
James M Hillis1,2,3, Bernardo C Bizzo1,4,3, Sarah F Mercaldo1,4,3
1Mass General Brigham AI, Boston, MA (J.M.H., B.C.B., S.F.M., A.G., A.L.M.D., M.A.H., A.S.S., E.L.I., V.T., K.J.D., B.M.S.).
Circulation. Heart failure
|May 14, 2025
概括
一个人工智能设备在使用心电图检测高性心肌病 (HCM) 方面表现有前途. 这种人工智能工具可以帮助早期诊断,并改善这种严重的心脏病患者的治疗结果.
科学领域:
- 心脏病学 心脏病学
- 医疗人工智能 医疗人工智能
- 诊断工具 诊断工具
背景情况:
- 增高性心肌病变 (HCM) 是致病率和死亡率的重要原因,特别是在年轻人中突然心脏死亡.
- 据估计,这种情况会影响500人中的1人,许多病例仍未被诊断出来.
- 改进的查方法,如心电图 (ECG) 分析,可以提高HCM的早期检测和诊断.
研究的目的:
- 评估人工智能 (AI) 设备在使用标准的12导电心电图来检测缩性心肌病 (HCM) 的准确性.
- 评估AI在增强HCM诊断能力方面的潜力.
主要方法:
- 使用基于深度学习的AI设备,提供二进制输出:"怀疑HCM"或"不怀疑".
- 这项研究包括一组数据集,包括293例HCM阳性病例和2912例HCM阴性病例,这些病例是通过对三家医院的图表审查确定的.
- 人工智能设备处理了291 (99.3%) 的HCM阳性和2905 (99.8%) 的HCM阴性病例.
主要成果:
- 人工智能设备在HCM检测方面实现了68.4%的灵敏度和99.1%的特异性.
- 曲线下的面积 (AUC) 为0.975,表明强烈的歧视性表现.
- 假设患病率为0.2% (500分之一),正预测值为13.7%,负预测值为99.9%.
结论:
- 人工智能设备在从12导电心电图中识别缩性心肌病症方面表现良好.
- 当与临床专业知识结合使用时,这种AI工具有可能改善HCM的检测和诊断.
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