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

Cerebral Palsy (CP), affecting approximately 1 in 500 children due to abnormal brain development, impacts movement control. Early risk assessment via the General Movements Assessment (GMA) at 3-4 months is highly predictive for CP but relies on trained clinicians. Machine-learning-based approaches for predicting GMA score from video have shown considerable promise, but are not openly available and rely on fine-tuned pre-processing steps, hand-crafted feature sets, and experimenter-driven hyperparameter selection. This, combined with strict privacy constraints on sharing data, limits the extent to which models can be trained and tested across datasets, thus reducing clinical impact. There is therefore a need to develop approaches that will work across different datasets to enable multi-site dataset aggregation and model training. To address this gap, we developed an end-to-end pipeline that uses off-the-shelf pose estimation, general-purpose feature extraction, and automated machine learning-none of which are tuned to a specific dataset. We applied this approach to a newly generated large dataset of 1063 infants (with approximately 12% positive class for adverse GMA outcome, drawn from a high-risk clinical cohort) within a preregistered study design. Model performance was evaluated on a strict "lock-box" validation set, which remained untouched during any phase of model development or pre-processing optimization. The developed model achieved moderate predictive accuracy for clinician-assessed GMA scores (Area Under the Receiver Operating Characteristic Curve, ROC-AUC = 0.79; Area Under the Precision-Recall Curve, PR-AUC = 0.34). The moderate accuracy is noteworthy given the 12% positive class prevalence. By releasing de-identified feature data and open-source code, and simplifying the training pipeline using automated machine learning, our work establishes essential groundwork for future robust, globally relevant CP screening tools suitable for low-resource settings.