AI-Generated Data Improves Multiple Sclerosis Classification in a Basal Ganglia Radiomics Model

Background: The heterogeneity and limited availability of magnetic resonance imaging (MRI) datasets in multiple sclerosis (MS) restrict the robustness of quantitative analysis methods like radiomics. This study explores the use of Generative Adversarial Networks (GANs) as a data harmonization technique. This study assesses whether GAN-generated images are realistic enough to improve the performance of radiomics-based classification models. Methods: We trained GANs to synthesize realistic T1w MRI from a cohort of MS patients and healthy controls. Segmented real and GAN-generated images were processed to extract statistics, texture, and shape radiomic features. Different machine-learning classifiers were trained using traditional augmentation techniques and cGAN-augmented datasets to assess pertinence. Explainable AI methods (SHAP) identified the most influential radiomic biomarkers and how they behave between real and GAN datasets. Results: GAN-generated images increased the mean classification accuracy when using a ResNet (from 0.88 to 0.98) on unseen test data. Explainability analyses revealed that texture heterogeneity and specific shape descriptors of the basal ganglia were the top predictors distinguishing MS from controls. Both datasets features show the same behavior when comparing their distributions. Conclusion: Integrating AI-powered synthetic MRI data into radiomic pipelines substantially improves disease classification accuracy and robustness. This approach addresses data scarcity due to differences in scan protocols, uncovers imaging biomarkers, and offers a scalable strategy for enhancing clinical decision support in MS diagnostics.