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Using expert-cited features to detect leg dystonia in cerebral palsy
Rishabh Bajpai1, Alyssa Rust1, Emma Lott1
1Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
Medrxiv : the Preprint Server for Health Sciences
|August 12, 2025
Summary
Leg dystonia in children with cerebral palsy (CP) is often missed. Machine learning models trained on quantified movement features achieved 82% accuracy in detecting leg dystonia from videos, improving diagnosis.
Area of Science:
- Neurology
- Biomedical Engineering
- Pediatrics
Background:
- Leg dystonia in cerebral palsy (CP) is a significant challenge, frequently underdiagnosed due to limitations in routine clinical evaluations.
- Current diagnostic accuracy for leg dystonia via standard clinical assessment is only 12%, highlighting a critical need for improved detection methods.
- Expert consensus identifies specific leg dystonia features, but objective quantification for diagnostic purposes has been lacking.
Purpose of the Study:
- To determine if expert-identified leg dystonia features in children with CP can be quantified from video recordings.
- To train machine learning (ML) models using these quantified features to accurately detect leg dystonia in children with CP.
- To develop a novel, accessible tool for improved leg dystonia diagnosis in pediatric CP patients.
Main Methods:
- Movement disorder specialists assessed videos of children with CP performing a seated task.
- Sixty-nine quantifiable features were extracted, corresponding to 12 expert-cited leg dystonia characteristics.
- Machine learning models were trained on quantified features from 163 videos and validated on 135 videos from two centers.
Main Results:
- ML models achieved 82% accuracy, 88% sensitivity, and 84% negative predictive value in identifying leg dystonia across both centers.
- Leg movement variability was a key indicator, comprising 68% of the top 25 features used by the best-performing models.
- The developed open-source software, DxTonia, utilizes these ML models for automated leg dystonia detection in CP videos.
Conclusions:
- Quantifying leg movement variability from videos provides an accurate method for detecting leg dystonia in children with CP.
- The DxTonia software significantly improves diagnostic accuracy for leg dystonia compared to traditional clinical evaluations.
- This approach offers a promising tool for earlier and more reliable diagnosis, potentially improving patient management and outcomes.
