摘要
由于脑疾病的发生会对社会产生严重危害,所以脑疾病诊断研究的重要性日益显著.中国"脑计划"列入"十三五"规划与国务院《"健康中国 2023"规划纲要》的印发表明国家对脑疾病诊疗问题的高度重视.由于磁共振影像的高分辨率及非入侵性等优势使其成为脑疾病研究与临床检查的主要技术手段,为脑疾病诊断提供丰富的数据基础.深度学习由于其可拓展性与灵活性在各个领域得到广泛应用,展现出巨大的发展潜力.本文针对深度学习在典型脑疾病诊断中的应用进行综述,结构组织如下:首先对深度学习在自闭症、精神分裂症、阿尔兹海默症三种典型脑疾病诊断上的应用进行了阐述;然后对用于三种脑疾病研究的数据集和已有的开源工具进行了汇总;最后对深度学习在磁共振影像脑疾病诊断应用中的局限性及未来发展方向进行总结与展望.
Abstract
As brain diseases can severely affect society,studies on the diagnosis of brain diseases are gaining importance.China is focused on counteracting the issues in brain disease diagnosis and treatment.Magnetic resonance imaging(MRI)has the advantages of high resolution and noninvasive nature,making it a preferred technique for brain disease research and clinical examination,providing rich databases for brain disease diagnosis.Deep learning is used in various fields due to its scalability and flexibility,and it has shown great potential for further development.Owing to recent developments in deep learning,it has made impressive achievements in various fields,such as computer vision and natural language processing,exhibiting great potential for its development and impact on brain disease diagnosis.Deep learning is being increasingly used for the diagnosis of brain disorders.We categorized studies reporting the use of deep learning for brain disease diagnosis by the type of disease to provide insights into the latest developments in this field.We cover the following aspects in this review.First,we reviewed and summarized the application of deep learning in the diagnosis of three typical brain disorders:autism spectrum disorder(ASD),schizophrenia(SZ),and Alzheimer's disease(AD).Second,we reviewed commonly used datasets and available open-source tools for diagnosing these three brain disorders.Finally,we summarized and predicted the application of deep learning in the diagnosis of brain disorders.The review focused on the diagnosis of the aforementioned brain disorders.ASD is a neurodevelopmental disorder that occurs in early childhood.SZ is a psychiatric disorder that occurs in young adulthood.AD is a brain disorder that commonly occurs in old age.We illustrated the application of deep learning in the diagnosis of these brain disorders based on the characteristics of their different inputs.While using MRI as an input source,most convolutional neural networks were used as backbone networks to design feature extraction methods.However,while working with data containing sequence information from many time points,recurrent neural networks were used to extract key information from the sequences.Apart from directly processing images as input,many studies extracted manual features,constructed graphs of manual features,and used graph neural networks for analysis.This approach yielded remarkable results.Moreover,our findings indicated that graph neural network-based analysis methods are being commonly used to diagnose brain disorders.
基金项目
国家自然科学基金资助项目(62172444)
湖南省自然科学基金资助项目(2022JJ30753)
中南大学创新驱动计划资助项目(2023CXQD018)
国家科技期刊平台
NSTL
维普
万方数据