心血管疾病/中风风险分层在深度学习框架:一个审查
Mrinalini Bhagawati1, Sudip Paul1, Sushant Agarwal2,3
1Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India.
Cardiovascular diagnosis and therapy
|July 5, 2023
概括
使用深度学习 (DL) 的心血管疾病 (CVD) 风险分层显示出希望. 集体DL方法正在成为单独和混合DL的优越方法,以准确,可靠的CVD风险评估.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
背景情况:
- 心血管疾病 (CVD) 仍然是全球主要的死亡原因,需要先进的,非侵入性的风险识别方法.
- 传统的CVD风险预测模型在多民族人群中与复杂的非线性关系作斗争.
- 现有的关于机器学习对心血管疾病风险的审查缺乏全面的深度学习 (DL) 集成.
研究的目的:
- 审查和分析单独深度学习 (SDL) 和混合深度学习 (HDL) 技术用于心血管疾病 (CVD) 风险分层.
- 在CVD风险评估中评估各种DL架构的特性,应用和验证.
- 识别和讨论用于心血管疾病风险分层的AI系统中的偏差风险.
主要方法:
- 在主要科学数据库中使用PRISMA模型对286个基于DL的CVD研究进行了系统审查.
- 分析重点是SDL和HDL架构,斑块组织表征和基于心电图 (ECG) 的解决方案.
- 使用PROBAST和ROBINS-I等工具进行了偏见风险评估,重点是基于UNet的框架中的地面真相选择.
主要成果:
- 卷积神经网络 (CNN) 算法由于自动特征提取而普遍存在.
- 基于集团的DL技术正在成为CVD风险分层的优越范式,超过SDL和HDL.
- DL方法在评估心血管疾病风险方面具有很高的准确性,可靠性和效率.
结论:
- 深度学习为非侵入性心血管疾病风险评估和分层提供了一种强大而有前途的方法.
- 整体DL方法有望成为CVD风险分层的主要技术.
- 减轻DL偏差风险需要多中心数据和严格的临床验证.
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