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针对个性化心电图诊断的深度学习的进展:一项系统性审查,解决患者间的变化和概括约束

Cheng Ding¹, Tianliang Yao², Chenwei Wu³

¹Georgia Institute of Technology, Department of Biomedical Engineering, Atlanta, United States.

Biosensors & bioelectronics

|December 21, 2024

概括

本综述探讨了针对个性化心电图 (ECG) 诊断的深度学习,解决了患者的变化. 这些发现突出了先进的人工智能技术,以实现更准确,以患者为中心的心脏护理.

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:

电心电图 (ECG) 解释传统上依赖于专家心脏病学家.
深度学习 (DL) 已经推进了医疗数据分析,包括心电图诊断.
在心电图数据中患者间的变化限制了受人口训练的人工智能模型的概括性.

研究的目的:

系统地审查最近的深度学习方法,以实现个性化的心电图诊断.
检查专门设计用于解决患者特异性的心电图解释变异性的模型.
识别个性化心电图AI的局限性和未来方向.

主要方法:

系统审查方法.
在四个主要数据库 (PubMed,IEEE Xplore,Web of Science,Google Scholar) 中搜索了2020-2024年的研究.
对112项选定的研究进行了严格的两步查和分析.

主要成果:

针对个性化心电图分析的高级DL技术的概述,包括转移学习,GAN,元学习和域调整.
分析处理患者特异性的心电图解释变化的方法.
识别诸如平衡概括与具体性和数据隐私等挑战.

结论:

深度学习在临床实践中具有个性化心电图诊断的变革潜力.
先进的DL技术为更准确,更有效,更以患者为中心的心脏诊断提供了途径.
这一综述为未来在个性化心脏护理方面的创新奠定了基础.

关键词:

深度学习是一种深度学习.域名适应领域适应生成性对抗性网络 (GANs) 是一种对抗性网络.医疗保健数据隐私医疗保健数据隐私个性化的心电图诊断

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