基于计算机辅助诊断技术的阿尔兹海默症早期分类研究综述

阿尔兹海默症（AD）作为主要的神经退行性疾病之一，已成为导致痴呆问题最常见的原因。截至目前，尚缺乏有效的针对性治疗药物和阻止疾病发展的有效治疗方式。随着计算机技术的不断发展，将计算机辅助诊断技术工具用于AD早期分类研究将为临床医生提供重要帮助。综述近些年来将传统机器学习和深度学习技术等手段用于AD的早期诊断分类的研究，研究样本主要为脑部神经成像数据（如MRI、PET）、脑电图（EEG）等生物标记物，结合机器学习方法对AD早期诊断进行分类研究。首先分析了将机器学习方法用于AD早期分类的应用，对比了采用不同算法的分类情况；其次，对比了针对受试者不同生物标记物以及采用单模态或不同模态组合方式用于AD早期分类的研究；最后介绍了AD分类面临的挑战并提出了未来的研究方向。

Review of early classification of Alzheimer's disease based on computer-aided diagnosis technology

As one of the major neurodegenerative diseases, Alzheimers disease (AD) has become the most common cause of dementia. Up to now, there is still a lack of effective targeted therapeutic drugs and treatments to prevent the progression of this disease. With the continuous development of computer technology, the use of computer-aided diagnosis technology tools for early AD classification research will provide clinicians with important help. We review the early diagnosis and classification of AD using traditional machine learning and deep learning techniques in recent years. Brain neuroimaging data (such as MRI, PET), electroencephalogram (EEG) and other biomarkers are mainly focused on. By combining machine learning, the classification of early diagnosis of AD is studied. Firstly, the application of machine learning methods to the early AD classification is analyzed, and the classification using different classification algorithms is compared. Secondly, the different biomarkers of subjects using single mode or multi-mode are compared to analyze early AD classification research. Finally, the challenges faced by AD classification are introduced, and future research directions are proposed.