综述机器学习在先心病诊断中的应用，梳理74项研究与数据集。
The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights
- 系统梳理2018-2024年432篇文献，聚焦74项关键研究。
- 总结常用数据集与算法，揭示先心病诊断的机器学习方案。
- 适合医疗AI研究者、临床医生参考，推动早期筛查技术发展。
先天性心脏病是常见的胎儿异常和出生缺陷之一。尽管已识别出诸多影响其发生的危险因素，但对其发生机制及在不同人群中的管理仍缺乏全面理解。近年来，机器学习在利用患者数据实现先天性心脏病早期检测方面展现出潜力。过去七年中，研究人员提出了多种数据驱动与算法解决方案。本文对基于机器学习的先天性心脏病识别进行了系统性综述，对2018至2024年间发表于顶级期刊的432篇参考文献进行元分析。通过对74篇学术著作的深入研究，揭示了数据库、算法、应用场景与解决方案等关键要素。此外，文中还列出了机器学习专家用于先天性心脏病识别的公开数据集。采用系统文献回顾方法，本研究识别出应用于先天性心脏病的机器学习所面临的挑战与机遇。
原文摘要 · Abstract (English)
Congenital heart disease is among the most common fetal abnormalities and birth defects. Despite identifying numerous risk factors influencing its onset, a comprehensive understanding of its genesis and management across diverse populations remains limited. Recent advancements in machine learning have demonstrated the potential for leveraging patient data to enable early congenital heart disease detection. Over the past seven years, researchers have proposed various data-driven and algorithmic solutions to address this challenge. This paper presents a systematic review of congential heart disease recognition using machine learning, conducting a meta-analysis of 432 references from leading journals published between 2018 and 2024. A detailed investigation of 74 scholarly works highlights key factors, including databases, algorithms, applications, and solutions. Additionally, the survey outlines reported datasets used by machine learning experts for congenital heart disease recognition. Using a systematic literature review methodology, this study identifies critical challenges and opportunities in applying machine learning to congenital heart disease.
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