论文标题
进入阿尔茨海默氏病进展评估：机器学习方法的综述
Towards Alzheimer's Disease Progression Assessment: A Review of Machine Learning Methods
论文作者
论文摘要
阿尔茨海默氏病（AD）是全球最具破坏性的神经退行性疾病，每年已达到近1000万例新病例。当前的技术提供了前所未有的机会来研究这种疾病的进展和病因，并具有先进的成像技术。随着大数据和机器学习（ML）驱动的一个社会的最新出现，研究人员付出了相当大的努力来总结基于ML的AD诊断的最新进展。在这里，我们概述了一些最普遍，最新的ML模型，用于评估AD的进展，并提供有关使用ML的AD研究中未来研究的挑战，机遇和未来方向的见解。
Alzheimer's Disease (AD), as the most devastating neurodegenerative disease worldwide, has reached nearly 10 million new cases annually. Current technology provides unprecedented opportunities to study the progression and etiology of this disease with the advanced in imaging techniques. With the recent emergence of a society driven by big data and machine learning (ML), researchers have exerted considerable effort to summarize recent advances in ML-based AD diagnosis. Here, we outline some of the most prevalent and recent ML models for assessing the progression of AD and provide insights on the challenges, opportunities, and future directions that could be advantageous to future research in AD using ML.
