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脑卒中医学中的机器学习和深度学习算法:对出血转变预测模型的系统审查

Mahbod Issaiy¹, Diana Zarei¹, Shahriar Kolahi¹

¹Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.

Journal of neurology

|December 12, 2024

概括

机器学习和深度学习模型显著提高了急性缺血性中风后出血变化的预测,优于传统方法. 这些先进的算法增强了临床决策和中风护理中的患者结果.

科学领域:

神经学神经学
人工智能在医学中的应用
生物医学信息学生物医学信息学

背景情况:

急性缺血性中风 (AIS) 具有显著的发病率和死亡率风险.
出血转化 (HT) 复杂化了AIS,使患者的预后恶化.
现有的评分系统缺乏足够的准确性来预测AIS中的HT.

研究的目的:

评估和比较机器学习 (ML) 和深度学习 (DL) 算法的预测性能,用于HT后AIS.
将ML和DL模型与传统预测模型进行基准测试.
评估先进AI在中风管理中的有效性.

主要方法:

在主要数据库 (PubMed,Embase,Web of Science,Scopus,IEEE) 进行系统的文献搜索.
在选了1421个初始记录后,包括了24项研究.
使用预测模型偏差风险评估工具 (PROBAST) 和定性合成进行质量评估.

主要成果:

梯度增强 (GB) 和卷积神经网络 (CNN) 显示出卓越的性能,中位数AUC为0.91.
机器学习模型,特别是那些整合临床和成像数据的模型,表现优于传统方法.
常见的ML算法包括逻辑回归 (LR),支持向量机 (SVM) 和随机森林 (RF),均值AUC分别为0.77,0.76和0.83.

结论:

在AIS中,ML和DL模型为HT预测提供了与传统评分系统相比的显著优势.
这些先进的AI模型可以增强临床决策和改善患者的治疗结果.
未来的研究应该专注于数据标准化,成像协议和模型透明度,以进一步优化中风护理.

关键词:

急性缺血性中风是一次急性缺血性中风.DL DL 是一个字.血流转变的发生.ML ML 在 ML 机器学习机器学习系统性审查系统性审查

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