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机器学习模型用于动脉斑块检测:基于超声波的诊断性能系统审查

Pooya Eini¹, Peyman Eini², Homa Serpoush¹

¹Cardiovascular Research Center, Rajaie Cardiovascular Institute, Tehran, Iran.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association

|September 7, 2025

概括

机器学习 (ML) 模型在超声波图像中检测动脉斑块时显示出高精度,为早期中风风险识别提供了有前途的工具. 由于研究异质性,需要进一步验证.

科学领域:

医疗成像医学成像
人工智能的人工智能
心血管疾病心血管疾病

背景情况:

动脉斑块是动脉样硬化和缺血性中风风险的关键指标.
手动超声波分析用于斑块检测是有限的,需要自动化解决方案.
机器学习 (ML) 提出了一种自动化动脉斑块检测的潜在方法.

研究的目的:

系统地审查和元分析ML模型的性能,以检测用超声波检测心血管斑块.
评估ML算法的诊断准确性,以识别动脉斑块.

主要方法:

按照PRISMA的指导方针,对主要数据库 (PubMed,Scopus,Embase,Web of Science,ProQuest) 进行了系统的文献搜索.
包括那些报告了基于ML的诊断指标,用超声波检测带斑块的研究.
使用元分析技术计算了聚合灵敏度,特异性和AUROC,并评估了偏差风险.

主要成果:

八项涉及200-19751名患者的研究进行了元分析.
最好的ML模型实现了0.94的聚合灵敏度,0.95的特异性和0.98的AUROC.
观察到高异质性 (I2 = 90%),但没有发现显著的出版偏差.

结论:

ML模型显示了通过超声波精确检测带斑块的巨大潜力.
这些发现支持ML在中风预防策略中的潜在临床整合.
由于当前研究中观察到的异质性和潜在偏差,标准化验证至关重要.

关键词:

动脉的斑块在动脉中形成.诊断表现的表现诊断表现机器学习是机器学习.预防中风预防中风超声波超声波是指超声波的使用.

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