PDF(Pubmed)
Abstract：
Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.
摘要：
脑中风,或者是脑血管意外,是一种破坏性的疾病，会破坏大脑的血液供应，剥夺它的氧气和营养。每一年,根据世界卫生组织的说法，全球有1500万人中风。这导致大约500万人死亡，另有500万人患有永久性残疾。各种风险因素的复杂相互作用凸显了迫切需要复杂的分析方法来更准确地预测中风风险并管理其结果。机器学习和深度学习技术通过分析包括患者人口统计在内的广泛数据集，提供有前途的解决方案。健康记录,和生活方式的选择，以揭示人类不容易辨别的模式和预测因素。这些技术实现了先进的数据处理，分析,和综合健康评估的融合技术。我们对2020年至2024年间发表的关于机器学习和深度学习在脑中风诊断中的应用的25篇综述论文进行了全面回顾。注重分类,分割，和物体检测。此外，所有这些评论都探讨了这些领域先进传感器系统的性能评估和验证，加强预测性健康监测和个性化护理建议。此外，我们还提供了一组用于脑中风分析的最相关的数据集.论文的选择是根据PRISMA指南进行的。此外，这篇综述严格地审查了每个领域，确定当前的挑战，并提出了未来的研究方向，强调人工智能方法在转变健康监测和患者护理方面的潜力。
