使用深度学习在全国多中心研究中对心脏病学图的自动解释
Chang Eun Park1, Byungjin Choi2,3, Rae Woong Park2
1Department of Convergence Healthcare Medicine, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.
Scientific reports
|June 4, 2025
概括
一个新的深度学习模型准确地解释心脏图解 (CTG) 信号以检测异常分娩. 这种自动CTG分析的进步有助于改善分娩期间的胎儿预后.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 产科和妇科 产科和妇科
背景情况:
- 及时检测异常心电图 (CTG) 对于分娩期间的胎儿预后至关重要.
- 现有的CTG解释的深度学习模型通常使用有限的数据集或专注于非临床结果.
- 深度学习在CTG分析中的临床适用性仍然是一个挑战.
研究的目的:
- 开发和验证用于自动CTG解释的临床适用的深度学习模型.
- 通过使用具有专家注释的大规模全国数据集来解决先前研究的局限性.
- 为了改善胎儿监测,对正常和异常CTG段进行分类.
主要方法:
- 利用了来自14家医院的22522次分娩的大规模数据集,CTG记录长达75分钟.
- 分段CTG信号分为5分钟间隔,创建一个数据集519800人分钟.
- 训练并验证了一种深度学习模型,用于对正常与异常CTG的二元分类.
主要成果:
- 在内部测试中,深度学习模型的AUC为0.880和PRC为0.625.
- 在三个数据集的外部验证结果显示,AUC从0.862到0.895不等,PRC从0.553到0.615.
- 在CTG分段的分类中表现出强的性能,表明自动化解释的潜力.
结论:
- 深度学习模型显示了对心脏图解信号的自动解释的巨大潜力.
- 开发的模型在大量多样化的数据集上进行训练,在识别异常CTG方面表现出强的表现.
- 未来的前性研究计划进一步评估这种自动化CTG分析工具的临床适用性.
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