人工智能对心声测量的自动化
Yuki Sahashi1, Hirotaka Ieki2, Victoria Yuan3
1Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Journal of the American College of Cardiology
|September 7, 2025
概括
这项研究介绍了EchoNet-Measurements,一个开源的人工智能工具,可以准确地自动进行心声回声测量. 这种深度学习模型可以帮助临床医生减少手动心声分析的负担.
科学领域:
- 心脏病学
- 医学成像
- 人工智能
背景情况:
- 准确的心声测量对于心血管疾病的诊断和监测至关重要.
- 手动心声图分析耗时且容易产生不准确性.
- 人工智能 (AI) 提供了一个解决方案来自动化这些测量,减少临床医生的工作量.
研究的目的:
- 开发和验证开源的深度学习语义细分模型.
- 在心声学中自动测量18个解剖学和多普勒参数.
- 根据专家超声波测试的评估,评估人工智能驱动测量的准确性和精度.
主要方法:
- 在Cedars-Sinai医疗中心 (CSMC) 的大型数据集上训练深度学习模型 (来自155,215项研究的877,983项测量).
- 使用CSMC的数据和斯坦福医疗保健 (SHC) 的外部数据集验证模型性能.
- 将人工智能生成的测量与超声波测量进行比较,以确定B模式和多普勒参数的准确性和精度.
主要成果:
- 开发了EchoNet-Measurements,这是一个开源的AI模型,用于自动化的心声标注.
- 在测量中实现了高精度,在内部 (CSMC) 和外部 (SHC) 验证数据集上表现出色.
- 在不同患者群体和设备供应商中表现一致,突出显示了稳健性.
结论:
- 通过EchoNet-Measurements提供高度准确的自动心声定量.
- 人工智能模型有很大的潜力帮助临床医生进行心声回应工作.
- 这种开源工具可以作为人工智能对心声学的未来发展的基础.
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