Risk prediction of prodromal Alzheimer's disease using deep learning of MRI scans. (31st December 2021)

• Record Type: Journal Article
• Authors: Basha, Shaikh; Marizzoni, Moira; Frisoni, Giovanni B; Jovicich, Jorge; Miyapuram, Krishna P
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-31
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.054803
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Deep learning approaches for classification have the advantage of automatically learning the relevant features but require large amount of data for training. We test the efficacy of deep learning approach for prediction of risk factor for prodromal Alzheimer's disease from whole brain MRI scans segmented into grey/white matter and CSF images. Method: Longitudinal MPRAGE images were acquired from 146 participants in six month intervals over a period of two years as part of PharmaCog Consortium. Whole brain structural scans were segmented using FreeSurfer pipeline into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) images. We used a convolutional neural network architecture with 11 layers to classify individuals with high and low risk based on 3‐dimensional MRI data. The model consisted of 3 convolution layers that performed slice wise convolutions in 2‐dimensions, 3 batch normalization layers, 1 max pooling layer, 1 flatten layer, 2 dense layers and 1 rectified linear unit (ReLU) activation layer. Hyperparameter tuning for the model was done using various combinations of kernel size ([2, 3, 4]), strides ([1, 2, 3]), batch size ([5, 10, 15, 20]), epochs ([10, 15, 20]) and number of neurons in the dense layer ([16, 32, 64]) to identify the optimized parameters for the model. Result: Out of the 146 subjects available, we chose 97 subjects randomly. The cross‐sectional MRI data at various timestamps was considered for training the model. The rest of the data was considered as the test set. After this separation, we had training size and test size of 436 and 168 images respectively. The training and test sets did not have any common subjects and thus provided relevant results for subject wise classification for unseen subjects. We trained and tested our model with all possible combinations of segmented images separately (GM, WM, CSF, GM+WM, WM+CSF, GM+CSF, GM+WM+CSF). Out of all the combinations, we attained best results using Grey mask (GM) data, with training and test accuracies of 93.62% and 72.02% respectively. Conclusion: We presented a deep learning framework for risk prediction of cross‐sectional whole brain MRI data for unseen subjects. This work combined with classification approaches on longitudinal data can prove effective in early diagnosis of Alzheimer's disease.