Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands; Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran.
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran.
Artif Intell Med. 2022 Aug;130:102332. doi: 10.1016/j.artmed.2022.102332. Epub 2022 Jun 12.
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a progressive decline in cognitive abilities. Since AD starts several years before the onset of the symptoms, its early detection is challenging due to subtle changes in biomarkers mainly detectable in different neuroimaging modalities. Developing computer-aided diagnostic models based on deep learning can provide excellent opportunities for the analysis of different neuroimage modalities along with other non-image biomarkers. In this survey, we perform a comparative analysis of about 100 published papers since 2019 that employ basic deep architectures such as CNN, RNN, and generative models for AD diagnosis. Moreover, about 60 papers that have applied a trending topic or architecture for AD are investigated. Explainable models, normalizing flows, graph-based deep architectures, self-supervised learning, and attention mechanisms are considered. The main challenges in this body of literature have been categorized and explained from data-related, methodology-related, and clinical adoption aspects. We conclude our paper by addressing some future perspectives and providing recommendations to conduct further studies for AD diagnosis.
阿尔茨海默病（AD）是一种不可逆转的神经退行性疾病，导致认知能力逐渐下降。由于 AD 在症状出现前几年就开始了，因此由于主要在不同神经影像学模式中检测到的生物标志物的细微变化，早期检测具有挑战性。基于深度学习的计算机辅助诊断模型可以为分析不同的神经影像模式以及其他非影像生物标志物提供极好的机会。在本调查中，我们对自 2019 年以来发表的约 100 篇论文进行了比较分析，这些论文采用了基本的深度学习架构，如 CNN、RNN 和生成模型，用于 AD 诊断。此外，还研究了大约 60 篇应用了热门主题或架构的论文。解释模型、归一化流、基于图的深度学习架构、自监督学习和注意力机制都被考虑在内。从数据相关、方法相关和临床采用方面对文献中的主要挑战进行了分类和解释。我们通过提出一些未来的观点并为 AD 诊断进一步研究提供建议来结束本文。
