Machine learning classification and regional differentiation of neuropathologically-confirmed Alzheimer's disease and comorbid Lewy body disease.

BACKGROUND: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) co-occur frequently, and growing evidence, including neuropathology, supports synergistic interplay between the diseases. We tested whether a single T1-weighted MRI scan may differentiate neuropathologically confirmed comorbid AD/DLB and AD controls using heterogeneously acquired neuroimaging. METHODS: We obtained structural neuroimaging, on two groups, AD with and without DLB pathology. Convolutional neural networks are trained across dimensions. We introduce a triple-ensemble strategy consisting of majority voting schemes within a variety of plane permutations. In addition, we conduct voxel-wise statistical analyses. RESULTS: Here we show convolutional neural networks record a classification accuracy of 0.820 and an f1 score of 0.79 in identifying comorbid DLB/AD from AD patients. Prediction accuracy is higher proximal to date of death, while the trained model largely outperforms clinical baseline diagnosis. The slice-level performance varies depending on the sampled brain location, with sensitivity highest in the temporal lobe and specificity highest in the occipital lobe. In DLB/AD, gray matter is relatively preserved though atrophy is observed in the occipital lobe, suggesting that the comorbidity differentially affects brain loss and may accelerate it in the occipital lobe. CONCLUSIONS: This study demonstrates how machine learning approaches can address diverse neuroimaging data from clinical sources to differentiate neurodegenerative diseases using a true gold standard of neuropathological confirmation. The frameworks utilized here can be extended to other diseases that are frequently co-occurring and feasibly extend to single scan diagnostic clinical utility of scans already being acquired.