Machine Learning Integrating Quantitative MRI and Clinical Features Predicts Loss of Ambulation in Duchenne Muscular Dystrophy

Background

Loss of ambulation (LOA) is a key milestone in Duchenne muscular dystrophy (DMD) with highly variable timing, complicating clinical counselling and trial design. Quantitative MRI non-invasively assesses muscle fat infiltration, yet few models have integrated MRI with clinical variables for individualized LOA prediction. We aimed to develop and validate a machine-learning model integrating quantitative MRI and clinical features to predict time to LOA and to derive interpretable, individualized risk estimates and candidate thresholds.