PDF(Pubmed)
Abstract：
UNASSIGNED: The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer\'s disease (AD), as the most common type of dementia, has become more frequent too.
UNASSIGNED: Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future.
UNASSIGNED: Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected.
UNASSIGNED: The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field.
UNASSIGNED: The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.
UNASSIGNED: Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future.
UNASSIGNED: Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected.
UNASSIGNED: The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field.
UNASSIGNED: The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.
摘要：
近几十年来越来越多的老年人导致了更普遍的老年病，比如中风和痴呆症。因此,阿尔茨海默病（AD），作为最常见的痴呆症类型，也变得更加频繁。
■目的：这项工作的目标是提出专注于AD及其早期自动诊断和预后的最新研究，主要是轻度认知障碍，并预测未来关于这一主题的研究可能会如何变化。
■现有文献中发现的文章需要满足几个选择标准。其中,他们的分类方法基于人工神经网络(ANN)，包括深度学习，并使用非来自脑信号或神经成像技术的数据。考虑到我们的选择标准，最后选出了过去十年发表的42篇文章。
■显示了医学上最重要的结果。发现了类似数量的基于浅层和深层人工神经网络的文章。递归神经网络和变压器在语音或纵向研究中很常见。卷积神经网络（CNN）在步态中很受欢迎，或者在模块化方法中与其他方法相结合。超过三分之一的横截面研究使用了多模态数据。非公共数据集经常用于横断面研究，而纵向相反。显示了最受欢迎的数据库，这将有助于未来该领域的研究人员。
■在过去十年中，CNN的引入及其在神经影像学数据方面的出色结果并未对其他模态的使用产生负面影响。事实上,新的出现了。
■目的：这项工作的目标是提出专注于AD及其早期自动诊断和预后的最新研究，主要是轻度认知障碍，并预测未来关于这一主题的研究可能会如何变化。
■现有文献中发现的文章需要满足几个选择标准。其中,他们的分类方法基于人工神经网络(ANN)，包括深度学习，并使用非来自脑信号或神经成像技术的数据。考虑到我们的选择标准，最后选出了过去十年发表的42篇文章。
■显示了医学上最重要的结果。发现了类似数量的基于浅层和深层人工神经网络的文章。递归神经网络和变压器在语音或纵向研究中很常见。卷积神经网络（CNN）在步态中很受欢迎，或者在模块化方法中与其他方法相结合。超过三分之一的横截面研究使用了多模态数据。非公共数据集经常用于横断面研究，而纵向相反。显示了最受欢迎的数据库，这将有助于未来该领域的研究人员。
■在过去十年中，CNN的引入及其在神经影像学数据方面的出色结果并未对其他模态的使用产生负面影响。事实上,新的出现了。
