使用人工智能启用心电图,预测心房的自发心脏转变
Brandon Wadforth1,2, Sobhan Salari Shahrbabaki1, Campbell Strong1
1College of Medicine and Public Health, Flinders University, Flinders Drive, Bedford Park, Adelaide, SA 5042, Australia.
支持人工智能的心电图可以预测心房患者的自发心脏转变,从而实现节省成本的等待方法. 这种AI-ECG策略可以减少住院和医疗保健费用.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 卫生经济学 卫生经济学
背景情况:
- 自发心脏转变 (SCV) 在急诊室 (ED) 患者中常见,患者患有原发性心房动 (AF).
- 预测SCV可以优化患者的出院并降低医疗保健成本.
- 目前在ED环境中预测SCV的方法有限.
研究的目的:
- 评估人工智能启用心电图 (AI-ECGs) 在预测初级AF患者的SCV中的有效性.
- 评估AI-ECG引导的"等待并观察"协议的潜在成本节省.
主要方法:
- 招募了患有初级AF的ED患者.
- 使用卷积神经网络 (CNN) 架构 (ResNet50,EfficientNet,DenseNet) 和用于SCV预测的集合学习模型.
- 进行了成本最小化分析,以评估AI-ECG协议的经济影响.
主要成果:
- 一个集体学习AI-ECG模型实现了69.7%的准确性和0.742.74的ROC AUC.
- 由AI-ECG指导的"等待"协议使每位患者的费用从4681美元降至3398.8美元.
- 这一协议导致整体住院病例减少了33.3%.
结论:
- AI-ECGs可以准确地预测SCV在患有初级AF向ED呈现的患者.
- 实施AI-ECG指导的"等待"协议可以大大节省成本,并减少住院病例.
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