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深度学习算法检测与结构性心脏病相关的声
John Prince1, John Maidens1, Spencer Kieu1
1Eko Devices, Inc. Oakland CA USA.
Journal of the American Heart Association
|October 13, 2023
概括
机器学习算法准确地检测结构性心脏声,超过临床医生. 这项技术有望改善早期诊断和治疗心脏病的患者护理.
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:
- 心脏听觉的准确性在医疗保健专业人员之间有很大的差异,这可能会延迟结构性心脏病的治疗.
- 尽管人们越来越感兴趣,但对于心脏听觉的机器学习 (ML) 算法很少达到临床实践.
- 一套新的深度学习训练,FDA批准的算法被评估为心脏声音分析.
研究的目的:
- 评估一套 FDA 批准的深度学习算法用于检测结构性心脏声的新套件的性能.
- 将算法的诊断准确性与模拟临床环境中的人类临床医生进行比较.
主要方法:
- 算法被训练在超过15000个心脏声音录音.
- 验证涉及来自615名受试者的2375条记录,这些记录是在临床环境中使用数字耳语镜收集的.
- 心声图作为诊断的黄金标准,算法性能与10名临床医生进行了比较.
主要成果:
- 该算法在检测结构声方面表现出85.6%的灵敏度和84.4%的特异性.
- 性能提高到97.9%的灵敏度和90.6%的特异性,对于明显可听的成年人语.
- 算法的平均准确度 (84.7%) 超过了临床医生 (77.9%),性能保持一致.
结论:
- 评估的ML算法准确地识别了与结构性心脏病相关的声.
- 该研究强调了算法的一致性与临床医生之间显著的观察者间变异性相比.
- 在临床实践中实施ML算法可以提高结构性心脏病检测和患者管理.
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