A Pre-Registered, Open Pipeline for Early Cerebral Palsy Risk Assessment from Infant Videos.

Area of Science:

• Neurology
• Developmental Pediatrics
• Biomedical Engineering

Background:

• Cerebral Palsy (CP) affects 1 in 500 children, impacting motor control due to abnormal brain development.
• Early detection via General Movements Assessment (GMA) at 3-4 months is crucial for CP risk assessment.
• Current machine learning models for GMA prediction lack generalizability and require dataset-specific tuning, hindering clinical application and data sharing.

Purpose of the Study:

• To develop a generalizable, end-to-end machine learning pipeline for predicting GMA scores from infant videos.
• To overcome limitations of existing models, such as dataset-specific tuning and privacy constraints, enabling multi-site data aggregation.
• To establish groundwork for robust, globally accessible CP screening tools, particularly for low-resource settings.

Main Methods:

• An end-to-end pipeline was created using off-the-shelf pose estimation and general-purpose feature extraction.
• Automated machine learning (AutoML) was employed, with no dataset-specific tuning.
• The approach was applied to a new dataset of 1063 infants from a high-risk cohort, with evaluation on a locked validation set.

Main Results:

• The model achieved moderate predictive accuracy for clinician-assessed GMA scores.
• Key performance metrics included an Area Under the Receiver Operating Characteristic Curve (ROC-AUC) of 0.79 and an Area Under the Precision-Recall Curve (PR-AUC) of 0.34.
• The performance is noteworthy given the 12% positive class prevalence in the dataset.

Conclusions:

• The developed pipeline offers a generalizable approach for GMA-based CP risk prediction.
• Open-sourcing code and de-identified data facilitates further research and development of CP screening tools.
• This work supports the creation of robust, accessible CP screening solutions for diverse clinical settings.