通过人工智能辅助的听觉检测心房动
Yongzhe Guo1, Huizhong Lin1, Hui Chen1
1Department of Cardiology, Fujian Institute of Coronary Heart Disease, Fujian Medical Center for Cardiovascular Diseases, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, Fujian Province, China.
BMC cardiovascular disorders
|November 7, 2025
概括
一个基于人工智能的电子听力镜有效地选心房动 (AF),显示出高精度和可靠性. 这种创新工具为在初级保健机构中广泛检测AF提供了一个有前途的解决方案.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗器械 医疗器械
背景情况:
- 心房动 (AF) 是一种常见的心律失常,对健康有重大影响.
- 传统的AF查方法有局限性,导致诊断不足.
- 这项研究评估了用于AF查的人工智能驱动的电子耳筒镜.
研究的目的:
- 评估基于人工智能的电子听筒仪在查心房动 (AF) 的有效性.
- 为了比较AI听力镜与其他便携式查设备的性能.
主要方法:
- 一个回顾性分析的496个心脏声音记录从患者和没有AF.
- 在衍生数据集上开发和培训AI模型 (ResNet34和Vision Transformer).
- 使用灵敏度,特异性,准确性,预测值和推导和验证数据集的AUC进行性能评估.
- 通过非连续一天的声音收集来评估模型的一致性.
主要成果:
- 人工智能模型表现出高性能,在两个数据集中精度约为0.92-0.93.
- 灵敏度在0.94-0.95之间,特异性在0.90-0.91.1之间.
- 在ROC曲线下的面积 (AUC) 始终很高 (0.92-0.93).
- 在不同天的声音采集中观察到良好的一致性 (科恩卡帕 = 0.74).
结论:
- 以人工智能为基础的电子耳语镜是可靠的AF查的有前途的工具.
- 它的可访问性使其适用于初级医疗保健和一般人口查.
- 这项技术有可能改善AF的早期检测和管理.
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