Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA.
Int J Mol Sci. 2021 Jul 24;22(15):7911. doi: 10.3390/ijms22157911.
A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.
目前越来越多的证据表明，深度学习方法可以成为阿尔茨海默病（AD）诊断和预测的重要基石。鉴于神经影像学和基因组学的最新进展，在最近的研究中，许多深度学习模型被用于区分 AD 与正常对照和/或区分 AD 与轻度认知障碍。在这篇综述中，我们专注于使用深度学习技术与神经影像学和基因组学原理相结合进行 AD 预测的最新进展。首先，我们叙述了利用深度学习算法利用基因组学或神经影像学数据建立 AD 预测的各种研究。特别是，我们描述了相关的整合神经影像学基因组学研究，这些研究利用深度学习方法，根据整合神经影像学和基因组学数据来预测 AD。此外，我们概述了神经影像学和基因组学方面最近关于深度学习的 AD 研究的局限性。最后，我们描述了对未来研究的挑战和方向的讨论。这项工作的主要新颖之处在于，我们总结了这些研究的要点，并仔细研究了这些研究之间的异同。
