Long range early diagnosis of Alzheimer's disease using longitudinal MR imaging data

•We trained a model to sequentially recognize different length partial MR image sequences from different stages of AD.•Our method is leveraged by the inexorably progressive nature of AD. To that end, a Temporally Structured Support Vector Machine (TS-SVM) model is proposed to constrain the partial MR image sequence’s detection score to increase monotonically with AD progression.•Furthermore, in order to select the best morphological features for enabling classifiers, we propose a joint feature selection and classification framework.