多国联合学习方法用于训练心电图和心声图模型以检测多变性心肌病
Shinichi Goto1,2,3, Divyarajsinhji Solanki1, Jenine E John1,2
1One Brave Idea and Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA (S.G., D.S., J.E.J., R.Y., M.H., C.A.M., R.C.D.).
Circulation
|August 2, 2022
概括
使用联合学习的机器学习模型显示,使用心电图和心声图来检测高性心肌病 (HCM) 的概括性得到了改善. 与单一机构模型相比,这种方法提高了不同患者队列的诊断准确性.
科学领域:
- 心脏病学
- 人工智能
- 医疗诊断
背景情况:
- 对新型向治疗来说,检测多变性心肌病 (HCM) 是非常重要的.
- 低患病率和常见疾病的症状重叠使得及时诊断成为一个挑战.
- 机器学习 (ML) 提供了自动化HCM检测和差异化的潜力.
研究的目的:
- 开发使用心电图和心声图的HCM检测ML模型.
- 在多个国际群体中增强模型的通用性.
- 区分HCM与其他心脏疾病,如大动脉狭窄,高血压和心脏粉症.
主要方法:
- 在外部数据上训练并验证单个机构的心电图模型.
- 在美国和日本使用多机构心电图和心声图模型的联合学习.
- 验证了持有试验组的模型,并评估了对其他心脏病的歧视.
主要成果:
- 联合学习模式在各个机构之间取得了很好的差异化 (C统计数据为0.90-0.96).
- 模型有效地区分了高血压,大动脉狭窄和心脏粉症 (C统计值为0.83-0.94).
- 自动检测显示出比心脏病学家手动解释更高的灵敏度.
结论:
- 联合学习显著提高了HCM检测模型的通用性.
- 使用心电图和心声图的ML模型可以可靠地检测和区分HCM.
- 这种方法提高了高性心肌病的诊断能力.
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