Machine learning classification and regional differentiation of neuropathologically-confirmed Alzheimer’s disease and comorbid Lewy body disease
- 1Department of Biomedical Engineering, Columbia University
- 2Department of Neurology, Columbia University Medical Center
Abstract
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.
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BibTeX
@article{park2026machine,
title = {{Machine learning classification and regional differentiation of neuropathologically-confirmed Alzheimer’s disease and comorbid Lewy body disease}},
author = {David K. Park and Allison B. Constant and Lawrence S. Honig and Karen S. Marder and Frank A. Provenzano and for the Alzheimer’s Disease Neuroimaging Initiative},
journal = {Communications Medicine},
year = {2026},
month = jun,
doi = {10.1038/s43856-026-01652-0},
url = {https://doi.org/10.1038/s43856-026-01652-0},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine learning classification and regional differentiation of neuropathologically-confirmed Alzheimer’s disease and comorbid Lewy body disease
AU - David K. Park
AU - Allison B. Constant
AU - Lawrence S. Honig
AU - Karen S. Marder
AU - Frank A. Provenzano
AU - for the Alzheimer’s Disease Neuroimaging Initiative
PY - 2026
DA - 2026/06/01
JO - Communications Medicine
DO - 10.1038/s43856-026-01652-0
UR - https://doi.org/10.1038/s43856-026-01652-0
AB - 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.
SN - 2730-664X
ER -