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The ability to predict the future trajectory of a patient is a key steptoward the development of therapeutics for complex diseases such as Alzheimer'sdisease (AD). However, most machine learning approaches developed forprediction of disease progression are either single-task or single-modalitymodels, which can not be directly adopted to our setting involving multi-tasklearning with high dimensional images. Moreover, most of those approaches aretrained on a single dataset (i.e. cohort), which can not be generalized toother cohorts. We propose a novel multimodal multi-task deep learning model topredict AD progression by analyzing longitudinal clinical and neuroimaging datafrom multiple cohorts. Our proposed model integrates high dimensional MRIfeatures from a 3D convolutional neural network with other data modalities,including clinical and demographic information, to predict the futuretrajectory of patients. Our model employs an adversarial loss to alleviate thestudy-specific imaging bias, in particular the inter-study domain shifts. Inaddition, a Sharpness-Aware Minimization (SAM) optimization technique isapplied to further improve model generalization. The proposed model is trainedand tested on various datasets in order to evaluate and validate the results.Our results showed that 1) our model yields significant improvement over thebaseline models, and 2) models using extracted neuroimaging features from 3Dconvolutional neural network outperform the same models when applied toMRI-derived volumetric features.

DeepAD: A Robust Deep Learning Model of Alzheimer's Disease Progression for Real-World Clinical Applications