Markerless pipeline estimates Rodda-Graham knee (R²=0.80) and ankle (R²=0.57) z-scores from single-view videos in 152 children with 60 diagnoses, achieving AUROC=0.88 for excess knee flexion screening.
Application of Deep Learning Models in Classification of Sagittal Gait Patterns Based on Rodda’s Classification System in Patients with Cerebral Palsy
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Quantifying Rodda and Graham Gait Classification from 3D Markerless Kinematics derived from a Single-view Video in a Heterogeneous Pediatric Clinical Cohort
Markerless pipeline estimates Rodda-Graham knee (R²=0.80) and ankle (R²=0.57) z-scores from single-view videos in 152 children with 60 diagnoses, achieving AUROC=0.88 for excess knee flexion screening.