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通过基于深度学习的儿科心电图来检测先天性心脏病,并与人类概念集成
Jintai Chen1, Shuai Huang2,3,4, Ying Zhang3,5
1State Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, 310009, Hangzhou, China.
Nature communications
|February 1, 2024
概括
一种新的深度学习模型,CHDdECG,有效地检测使用心电图的儿童先天性心脏病 (CHDs). 这种非侵入性方法显示出高精度,在早期发现心血管疾病方面超过了人类专家.
科学领域:
- 儿童心脏病学 儿童心脏病学
- 人工智能在医学中的应用
- 生物医学信号处理
背景情况:
- 早期发现先天性心脏病 (CHD) 对于改善儿科患者治疗结果至关重要.
- 目前的检测方法可能需要侵入性程序或缺乏成本效益.
- 对儿科心脏病检测的非侵入性,低成本的技术是非常可取的.
研究的目的:
- 开发和验证一种深度学习方法,CHDdECG,用于使用儿科心电图 (ECG) 自动检测CHD.
- 评估CHDdECG与人类专家诊断的性能.
- 探索自动提取的心电图特征与人类定义的特征在心脏病检测中的贡献.
主要方法:
- 开发了一个深度学习模型,CHDdECG,可以自动从儿科心电图和波形变换中提取特征.
- 该模型将自动提取的特征与关键的人类概念特征集成.
- 该模型在65,869个案例中接受了训练,并在多个现实世界和外部测试集 (共超过27,000个案例) 中得到了验证.
主要成果:
- 在12,000个病例的试验组中,CHDdECG的ROC-AUC为0.915和特异性为0.881.
- 外部验证显示了强的性能,整体ROC-AUC为0.917和0.907,特异性为0.937和0.907.
- 在心脏病检测准确度方面,CHDdECG的表现优于心脏病学家,自动化ECG特征比人类概念特征更重要.
结论:
- 拟议的CHDdECG深度学习模型提供了一种高效且非侵入性的方法,用于使用心电图进行儿科心脏病检测.
- 该模型的性能超过了人类专家的性能,这表明它可以识别超出人类认知能力的模式.
- 使用深度学习的儿科心电图分析具有促进心脏病诊断和改善患者治疗结果的巨大潜力.
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Introduction
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