VGG-TSwinformer: Transformer-based deep learning model for early Alzheimer's disease prediction. (February 2023)

• Record Type: Journal Article
• Authors: Hu, Zhentao; Wang, Zheng; Jin, Yong; Hou, Wei
• Is Part Of: Computer methods and programs in biomedicine. Volume 229(2023)
• Publication Date: 2023-02
• Subjects: Alzheimer's disease (AD) -- Deep learning -- Longitudinal study -- Convolutional neural network (CNN) -- Transformer; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107291
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A short-term longitudinal study of MCI based on deep learning. The progressive nature of MCI disease was considered. Combining temporal and spatial attention to establish a pattern of brain morphological changes in MCI patients. Transformer provides a new solution for the application of deep learning in longitudinal disease research. Abstract: Background and objective: Mild cognitive impairment (MCI) is a transitional state between normal aging and Alzheimer's disease (AD), and accurately predicting the progression trend of MCI is critical to the early prevention and treatment of AD. Brain structural magnetic resonance imaging (sMRI), as one of the most important biomarkers for the diagnosis of AD, has been applied in various deep learning models. However, due to the inherent disadvantage of deep learning in dealing with longitudinal medical image data, few applications of deep learning for longitudinal analysis of MCI, and the majority of existing deep learning algorithms for MCI progress prediction rely on the analysis of the sMRI images collected at a single time-point, ignoring the progressive nature of the disorder. Methods: In this work, we propose a VGG-TSwinformer model based on convolutional neural network (CNN) and Transformer for short-term longitudinal study of MCI. In this model, VGG-16 based CNN is used to extract low-level spatial features of longitudinal sMRI images and map these low-level features to high-level feature representations, sliding-window attention is used for fine-grained fusion of spatially adjacent feature representations, and gradually fuses distant spatial feature representations through the superposition of attention windows of different sizes, temporal attention is used to measure the evolution of this feature representations as a result of disease progression. Results: We validated our model on the ADNI dataset. For the classification task of sMCI vs pMCI, accuracy, sensitivity, specificity and AUC reached 77.2%, 79.97%, 71.59% and 0.8153 respectively. Compared with other cross-sectional studies also applied to sMRI, the proposed model achieved better results in terms of accuracy, sensitivity, and AUC. Conclusion: The proposed VGG-TSwinformer is a deep learning model for short-term longitudinal study of MCI, which can build brain atrophy progression model from longitudinal sMRI images, and improve diagnostic efficiency compared to algorithms using only cross-sectional sMRI images.