Magnetic resonance imaging (MRI) plays an increasingly important role in the diagnosis and prognosis of neurodegenerative diseases. One field of extensive clinical use of MRI is the accurate and automated classification of degenerative disorders. Most of current classification studies either do not mirror medical practice where patients may exhibit early stages of the disease, comorbidities, or atypical variants, or they are not able to produce probabilistic predictions nor account for uncertainty. Also, the spatial heterogeneity of the brain alterations caused by neurodegenerative processes is not usually considered, despite the spatial configuration of the neuronal loss is a characteristic hallmark for each disorder. In this article, we propose a classification technique that incorporates uncertainty and spatial information for distinguishing between healthy subjects and patients from four distinct neurodegenerative diseases: Alzheimer's disease, mild cognitive impairment, Parkinson's disease, and Multiple Sclerosis. We introduce a spatially informed Bayesian neural network (SBNN) that combines a three-dimensional neural network to extract neurodegeneration features from MRI, Bayesian inference to account for uncertainty in diagnosis, and a spatially informed MRI image using hidden Markov random fields to encode cerebral spatial information. The SBNN model demonstrates that classification accuracy increases up to 25% by including a spatially informed MRI scan. Furthermore, the SBNN provides a robust probabilistic diagnosis that resembles clinical decision-making and can account for the heterogeneous medical presentations of neurodegenerative disorders.
Keywords: Bayesian inference; classification; deep learning; magnetic resonance imaging; neurodegenerative diseases; spatial information.
© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.