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 two 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 4664 ML models on 163 videos from one center, and tested the best performing models on a separate se

SUBMITTER: Bajpai R 

PROVIDER: S-EPMC12338898 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature