机器学习方法用于检测冠状动脉疾病使用血管图像:一个范围审查
Fatemeh Rangraz Jeddi1, Hasan Rajabi Moghaddam2, Reihane Sharif1
1Health Information Management Research Center, Department of Health Information Management & Technology, School of Allied Health Professions, Kashan University of Medical Sciences, Kashan, Iran.
Studies in health technology and informatics
|June 30, 2023
概括
机器学习 (ML) 通过血管学有效检测冠状动脉疾病 (CAD). 临床整合需要进一步发展,以改善CAD诊断和管理.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 冠状动脉疾病 (CAD) 的诊断严重依赖于成像.
- 机器学习 (ML) 为增强CAD检测提供了潜力.
- 目前关于通过血管学检测CAD的ML的文献需要综合.
研究的目的:
- 识别和总结ML方法用于CAD检测使用血管图像.
- 审查应用的血管造影类型和ML算法.
- 评估这些方法的性能和临床适用性.
主要方法:
- 对多个数据库进行范围审查.
- 包括使用计算机断层扫描和侵入性冠状动脉血管学的23项研究.
- 分析各种ML算法,包括深度学习 (CNN,U-Net).
主要成果:
- 机器学习算法在提高CAD检测的准确性和效率方面表现有前途.
- 深度学习模型,如CNN和U-Net变体经常被使用.
- 性能因数据集,算法和功能选择而有所不同.
结论:
- ML方法显示出增强血管学CAD检测的潜力.
- 标准化和验证对于临床整合至关重要.
- 需要进一步的研究来开发针对CAD的强大,临床适用的ML工具.
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