机器学习用于外围动脉疾病诊断和管理的应用:系统性审查
Negar Aant1, Masoud Arabbeiki2, Mohammad Reza Niroomand3
1Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129, Turin, Italy.
Computers in biology and medicine
|July 11, 2025
概括
机器学习 (ML) 有助于诊断外围动脉疾病 (PAD) 并指导其管理. 这次审查强调了ML的ML.
科学领域:
- 心血管医学 心血管医学
- 人工智能的人工智能
- 生物医学信息学 生物医学信息学
背景情况:
- 周围动脉疾病 (PAD) 是由动脉样硬化引起的严重疾病,导致下肢动脉狭窄,血液流量减少,肢体损失和死亡风险增加.
- 早期诊断和有效管理PAD对于改善患者的治疗结果和预防严重并发症至关重要.
研究的目的:
- 系统地审查机器学习 (ML) 算法在周围动脉疾病 (PAD) 的诊断和管理中的应用.
- 分析PAD研究中使用的ML模型的数据类型,特性,性能指标,软件工具和结果.
主要方法:
- 在PubMed,Scopus和Web of Science进行了全面的文献搜索,确定了2014年至2024年间发表的30项相关研究.
- 该审查涵盖了各种ML领域,包括回归,分类和聚类,利用神经网络,集合学习和深度学习等技术.
- 在五个领域进行了偏差风险评估,以评估包含的研究的可靠性.
主要成果:
- 临床记录是主要的数据来源,在大约50%的审查研究中使用.
- 随机森林算法是PAD分析中最常用的ML技术.
- 机器学习模型展示了多功能性,适用于诊断和风险评估数据集,50%的研究显示偏差风险低.
结论:
- 机器学习提供了一种强大的,非侵入性的方法,可以提高对外围动脉疾病的诊断和管理.
- 这些发现强调了ML在识别大型数据集中的复杂模式方面的重大潜力,以改善PAD患者的护理.
关键词:
人工智能的人工智能是人工智能.动脉样硬化是一种动脉样硬化.深度学习是一种深度学习.骨 Femoropopliteal 动脉 的 动脉 的 动脉 的 动脉 .机器学习是机器学习.周围动脉疾病是什么?更多相关视频
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