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用机器学习方法区分COVID-19相关的急性缺血性中风的诊断模型.

Eylem Gul Ates1,2, Gokcen Coban3, Jale Karakaya2

  • 1Institutional Big Data Management Coordination Office, Middle East Technical University, 06800 Ankara, Türkiye.

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概括

这项研究表明,人工智能可以分析脑部MRI扫描来检测急性缺血性中风 (AIS) 患者的COVID-19. 机器学习模型有效地区分COVID-19阳性和阴性病例,帮助诊断.

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科学领域:

  • 神经学 神经学
  • 放射学 放射学是一门学科.
  • 人工智能的人工智能

背景情况:

  • COVID-19越来越多地与神经系统并发症有关,包括急性缺血性中风 (AIS).
  • 区分与COVID-19相关的AIS需要先进的诊断工具.

研究的目的:

  • 评估脑MRI和机器学习的放射性特征在AIS患者中识别COVID-19的有效性.
  • 开发和评估人工智能驱动的模型来诊断COVID-19相关的AIS.

主要方法:

  • 从57名AIS患者 (30名COVID-19阳性,27名阴性) 的脑部MRI数据的回顾性分析.
  • 从MRI中提取放射性特征,然后使用Boruta,LASSO和RFE等算法进行特征选择.
  • 机器学习分类器 (ANN,k-NN,RF,SVM) 的培训和评估,以区分COVID-19阳性和阴性组.

主要成果:

  • 使用k-NN分类器的递归特征消除 (RFE) 方法获得了最高的性能,曲线下的面积 (AUC) 为0.882%,准确率为79.1%.
  • 人工神经网络 (ANN) 和k-最近邻居 (k-NN) 显示出强大的区分能力,AUC分别高达0.857和0.863,没有特征选择或Boruta选择.
  • 这些模型显示出高诊断可靠性,特定的分类器表现出优异的特异性和积极的预测值 (PPV).

结论:

  • 放射学分析与机器学习相结合,提供了一种有效的方法来区分COVID-19相关的AIS和非COVID-19的AIS,使用大脑MRI.
  • 基于人工智能的诊断工具显示出早期检测高风险患者,优化治疗和改善COVID-19神经复杂症的临床结果的巨大潜力.