Using Expert-Cited Features to Detect Leg Dystonia in Cerebral Palsy

Abstract

Objectives: Leg dystonia in cerebral palsy (CP) is debilitating but remains underdiagnosed. Routine clinical evaluation has only 12% accuracy for leg dystonia diagnosis compared to gold-standard expert consensus assessment. We determined whether expert-cited leg dystonia features could be quantified to train machine learning (ML) models to detect leg dystonia in videos of children with CP.

Methods: Eight pediatric movement disorders physicians assessed 298 videos of children with CP performing a seated task at 2 CP centers. We extracted leg dystonia features cited by these experts during consensus-building discussions, quantified these features in videos, used these quantifications to train 4,664 ML models on 163 videos from one center, and tested the best performing models on a separate set of 135 videos from both centers.

Results: We identified 69 quantifiable features corresponding to 12 expert-cited leg dystonia features. ML models trained using these quantifications achieved 88% sensitivity, 74% specificity, 82% positive predictive value, 84% negative predictive value, and 82% accuracy for identifying leg dystonia across both centers. Of the 25 features contributing to the best performing ML models, 17 (68%) quantified leg movement variability. We used these ML models to develop DxTonia, open-source software that identifies leg dystonia in videos of children with CP.

Interpretation: DxTonia primarily leverages detection of leg movement variability to achieve 82% accuracy in identifying leg dystonia in children with CP, a significant improvement over routine clinical diagnostic accuracy of 12%. Observing or quantifying leg movement variability during a seated task can facilitate leg dystonia detection in CP. ANN NEUROL 2026.