基于深度学习的12导电心电图用于检测患者的左下心室喷射断裂
Yuxin Hou1, Zhiping Fan2, Jiaqi Li1
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China; Centre for Collaborative Research, Shanghai University of Medicine and Health Sciences, Shanghai, China.
The Canadian journal of cardiology
|September 29, 2024
概括
一个人工智能支持的心电图 (AI-ECG) 算法准确地识别了左心室喷射率 (LVEF) 减少的患者,这是心力衰竭的关键指标. 这种AI-ECG工具提供了高效,快速和成本效益的早期心力衰竭查.
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:
- 减少左心室喷射率 (LVEF) 是心力衰竭的主要指标,需要早期检测以有效管理和降低死亡率.
- 迅速识别低射出分数对于及时干预和改善心力衰竭管理患者结果至关重要.
研究的目的:
- 开发和验证人工智能启用心电图 (AI-ECG) 算法,用于识别低射出率患者.
- 使用AI-ECG技术预测左心室喷射率 (LVEF) 值.
- 评估算法的准确性和效率,作为早期心力衰竭检测的查工具.
主要方法:
- 利用心电图 (ECG) 数据作为人工智能算法的输入来预测低射出分数概率和估计LVEF值.
- 对最初正常LVEF的个体进行了为期5年的随访研究.
- 使用密集护理-IV (MIMIC-IV) 数据库进行外部验证,以评估算法性能.
主要成果:
- AI-ECG算法在测试组上检测LVEF≤50%,达到0.965的曲线下的面积 (AUC),准确度为92.8%,灵敏度为88.8%,特异性为92.9%.
- 对于LVEF回归,该算法在测试组中显示的平均绝对误差为5.28,在外部验证中显示的平均误差为9.56 (AUC为0.848).
- 假阳性结果与发展低射出分数 (26.2%对2.0%;P < 0.0001) 的可能性显著增加.
结论:
- AI-ECG算法准确地识别了低射出分数,证明了早期心力衰竭检测的有效性.
- 人工智能-心电图算法作为一种高效,快速和具有成本效益的查工具,用于识别心力衰竭风险的个体.
- 这项技术具有改善早期心力衰竭诊断和管理的巨大潜力.
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