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

backgroundAlzheimer'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.

methodsWe 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.

resultsHere 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.

conclusionsThis 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.