Deep Learning Classification of Muscular Dystrophy from MR Images using Swin Transformer

Motivation: This study aims to improve the accuracy of muscular dystrophy (MD) diagnosis by applying AI and multiparametric MRI to distinguish subtypes with similar muscle involvement patterns. Goal(s): The primary goal is to develop a Swin Transformer (SwinT) AI-based classification approach for BMD, LGMD2, and healthy subjects using muscle MR images and identify the optimal MRI contrast for accurate classification. Approach: In a retrospective study, we utilized SwinT and VGG19 AI models with various MRI contrasts in a 10-fold cross-validation setup. Results: SwinT outperformed VGG19, with the Fat Fraction contrast delivering the highest accuracy of 89.3%±4.9%, highlighting the potential for more accurate MD diagnosis. Impact: This work could improve muscular dystrophy diagnosis, offering clinicians a more objective and accurate tool. Patients may benefit from earlier and more precise interventions, while scientists can explore novel research avenues in AI-driven medical diagnostics, ultimately reducing healthcare disparities.