从声心图记录中检测心脏声:乔治·B·穆迪的PhysioNet挑战2022年
Matthew A Reyna1, Yashar Kiarashi1, Andoni Elola2
1Department of Biomedical Informatics, Emory University, Atlanta, Georgia, United States of America.
PLOS digital health
|September 11, 2023
概括
乔治·B·穆迪PhysioNet挑战赛2022开发了算法,从心脏声音记录中检测心脏声,改善了可访问的心脏查. 开源算法和新型指标提高了资源有限的环境中的可重现性和临床相关性.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 人工智能在医学中的应用
背景情况:
- 心脏听觉是心脏声的重要查工具,但需要专家的解释,限制其在资源有限的地区的使用.
- 声心电图 (PCG) 记录为远程心脏评估提供了一个潜在的替代方案.
- 乔治·B·穆迪物理网络挑战赛2022旨在通过促进算法解决方案来解决这些局限性.
研究的目的:
- 开发和评估使用PCG记录检测心脏声和心脏功能异常的算法方法.
- 通过开源代码提交和新的评估指标,促进透明度,可复制性和临床相关性.
- 探索人工智能驱动的心脏查在服务不足地区的潜力.
主要方法:
- 来自巴西农村的1452名儿科患者的5272个PCG记录.
- 邀请团队开发并提交用于声检测和心脏功能评估的算法.
- 要求提交所有算法的完整训练和推理代码.
- 设计了一种新的评估指标,包括查,诊断和治疗成本.
主要成果:
- 接收了87个团队的779个算法,产生了53个功能代码库.
- 算法采用了各种方法,包括传统的机器学习和深度学习.
- 证明了对心脏声音进行算法分析的潜力,以实现可访问的诊断查.
结论:
- 对PCG录像的算法解释显示,它有望扩大心脏查的可访问性.
- 开源提交和量身定制的评估指标提高了开发算法的可靠性和适用性.
- 该挑战促进了人工智能在心血管诊断方面的进步,特别是在资源有限的环境中.
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