本文以人工智能技术在脑卒中筛查领域的研究进展为重点，围绕使用神经网络及深度学习算法建立卒中筛查模型，智能化评估算法的研究方向，结合行业各类研究及应用情况进行了综述分析。人工智能技术在卒中筛查及高危人群筛查预防领域的研究应用已有开展，但目前主要问题是积累的筛查数据量小，缺少标准化的、适用于人工智能研究的筛查数据集，且由于特殊数据及危险因素划定原因，其预测准确率与传统统计学分析方法相比差异较大。综合已有的研究成果及应用情况可见，关于人工智能技术在该领域的主要研究应先建立标准化数据集，并采用适合于结构化特征数据的深度学习算法用于脑卒中的智能化筛查研究。

This article reviews mainly the advancement of artificial intelligence technology in stroke screening, and analyses the research directions of intelligent assessment algorithms based on the establishment of stroke screening by using neural networks and deep learning algorithms. Artificial intelligence application has been emerging in both regular population screening of stroke and screening and prevention of stroke for a high-risk population. At present, the main problems, however, are the small amount of accumulated screening data, the lack of standardized and AI-suitable datasets, and the discrepancy in predictive accuracy between AI-assisted screening and traditional analysis methods due to particular data and undefined risk factors. Therefore, we should establish standardized datasets and adopt deep learning algorithms suitable for structured feature data for intelligent screening research of stroke.