From Longitudinal Measurements to Image Classification: Application to Longitudinal MRI in Alzheimer’s Disease

Abstract

We propose a novel method of constructing representations of multiple one-dimensional longitudinal measurements as two-dimensional grey-scale images. This can be used to turn classification problems from longitudinal settings into simpler image classification problems, allowing for the application of newer deep learning methods on longitudinal measurements. Our approach is applicable to situations with balanced or imbalanced longitudinal data sets, and where there are missing data at some time points. To evaluate our approach, we apply it to an important and challenging task: the prediction of dementia from brain volume trajectories derived from longitudinal MRI. We construct an ensemble of convolutional neural network models to classify two groups of subjects: those diagnosed with mild cognitive impairment at all examinations (stable MCI) versus those starting out as MCI but later converting to Alzheimer's disease (converted AD). Models were trained on image representations derived from N = 736 subjects sourced from the ADNI database (471/265 sMCI/cAD). We obtained an accuracy of a resulting ensemble model of 76%, measured on an independent test set. Our approach is simple and easy to apply but competitive (in terms of accuracy) with results reported in other machine learning approaches with similar classification on comparable tasks. This indicates that our approach can lead to useful representations of longitudinal data.