Deep Learning Modeling to Differentiate Multiple Sclerosis From MOG Antibody-Associated Disease
Rosa Cortese1, Francesco Sforazzini2, Giordano Gentile1,2
1Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
Neurology
|September 4, 2025
Summary
A new deep learning model and a clinical/MRI algorithm accurately distinguish myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) from multiple sclerosis (MS). Combining both approaches improved diagnostic accuracy, aiding in differentiating these neurological conditions.
Area of Science:
- Neurology
- Radiology
- Artificial Intelligence
Background:
- Multiple sclerosis (MS) is a common adult neurological disorder, whereas myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is rare.
- Distinguishing MOGAD from MS is crucial for appropriate treatment, as their management differs significantly.
- Previous machine-learning models showed promise but required further validation.
Purpose of the Study:
- To validate a clinical/MRI algorithm for differentiating MS from MOGAD.
- To develop and evaluate a deep learning (DL) model for the same diagnostic task.
- To assess the combined performance of both models and identify key differentiating imaging features using probability attention maps (PAMs).
Main Methods:
- A multicenter retrospective study involving 406 MRI scans from adults with MS and MOGAD.
- Performance evaluation of a pre-existing clinical/MRI algorithm on a validation dataset.
- Development and testing of a ResNet-10 convolutional neural network-based DL classifier on independent datasets.
- Generation of PAMs to visualize and quantify differentiating brain regions.
Main Results:
- The clinical/MRI algorithm achieved 75% accuracy, 96% sensitivity, and 56% specificity in the validation cohort.
- The DL model demonstrated 70% accuracy, 67% sensitivity, and 73% specificity in the validation cohort.
- Combining both models yielded 86% accuracy, 84% sensitivity, and 89% specificity.
- PAMs highlighted distinct lesion patterns, with corpus callosum, precentral gyrus, thalamus, and cingulate cortex differentiating MS, while the brainstem, hippocampus, and parahippocampal gyrus indicated MOGAD.
Conclusions:
- Both the clinical/MRI algorithm and the DL model are effective in distinguishing MS from MOGAD.
- The models have complementary strengths, with the clinical/MRI algorithm showing higher sensitivity and the DL model higher specificity.
- The combination of both approaches significantly enhances diagnostic accuracy.
- PAMs provide valuable insights into the distinct neuropathological patterns of MS and MOGAD, aiding differential diagnosis.
