Keywords: Diagnosis/Prediction, Muscle, Deep Learning; Classification; MRI; Swin Transformer; Generative AI; Dystrophy
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.
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