Evaluation of Deep Learning Algorithms to Predict Multiple Dementia-Related Neuropathologies from Brain MRI, Clinical and Genetic Data

Alzheimers disease and related dementias (ADRD) involve overlapping neurodegenerative and vascular pathologies--such as amyloid-{beta} (A{beta}), tau, cerebral amyloid angiopathy (CAA), TDP-43, and alpha-synuclein--that complicate diagnosis and treatment. While PET and CSF biomarkers are useful for detecting A{beta} and tau, they are invasive, expensive, and not widely available. In contrast, magnetic resonance imaging (MRI) is non-invasive and widely accessible, offering an opportunity for pathology prediction when combined with deep learning. Most prior studies have focused on single-pathology detection, but there remains a need for models that can jointly predict multiple co-occurring pathologies. In this work, we evaluate deep learning models that integrate structural MRI with demographic, clinical, and genetic data to classify six autopsy-confirmed neuropathologies: A{beta}, tau, CAA, TDP-43, hippocampal sclerosis, and dementia with Lewy bodies. We compare our hybrid deep learning model to AutoGluon, an automated machine learning framework. Our findings support the potential of multimodal AI to enable non-invasive, comprehensive neuropathological profiling in ADRD.