评估卷积神经网络增强型心电图,用于在专业心血管环境中检测多变性心肌病
Naomi Hirota1, Shinya Suzuki2, Jun Motogi3
1Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan. n-hirota@cvi.or.jp.
Heart and vessels
|March 30, 2024
概括
卷积神经网络 (CNN) 增强的心电图 (ECG) 显示出检测多变性心肌病 (HCM) 和扩张性心肌病 (dHCM) 的前景. 虽然初始精度较低,但针对特定患者和使用亚型模型显著提高了诊断准确度.
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:
- 缩性心肌病变 (HCM) 和扩张性心肌病变 (dHCM) 是重要的心脏疾病.
- 卷积神经网络 (CNN) 增强心电图 (ECG) 对HCM和dHCM的现实诊断效果尚未得到充分证实.
- 准确和早期发现HCM和dHCM对于患者的管理和结果至关重要.
研究的目的:
- 评估CNN增强型心电图用于检测HCM和dHCM的现实世界诊断性能.
- 为了比较基本诊断模型与综合诊断模型 (包括亚型) 的有效性.
- 评估不同ECG导线配置对诊断准确性的影响.
主要方法:
- 来自Shinken数据库 (2010-2017年) 的19170名患者心电图的回顾性分析,其中140名患有HCM或dHCM.
- 开发和评估两种CNN增强的ECG模型:"基本诊断"模型和"综合诊断"模型.
- 对不同诊断概率和领先配置的灵敏度,积极预测率 (PPR) 和F1得分的评估.
主要成果:
- "基本诊断"模型使用全心电图实现了76%的灵敏度,2.9%的PPR和0.056的F1得分.
- 在患有诊断概率≥0.9和心电图左心室缩 (LVH) 的患者中,表现显著改善:100%的灵敏度,8.6%的PPR,0.158的F1得分.
- "综合诊断"模型进一步增强了100%灵敏度,13.0%PPR和0.230F1得分的指标. 性能与较少的线索一致,特别是那些查看侧墙的线索.
结论:
- 通过CNN增强的心电图显示了HCM和dHCM检测的潜力,在专注于高概率病例和纳入疾病亚型时,准确度提高.
- 虽然最初的真实世界的精度很低,但有针对性的应用和全面的模型可以提高诊断价值.
- 包括横向导线在内的心电图配置是有效的,这表明在不影响性能的情况下减少导线使用的潜力.
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