Automating the General Movements Assessment: Potential, pitfalls, and a path forward

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This research aims to explore the effectiveness of automating the General Movements Assessment and its implications for diagnosing cerebral palsy in infants.
Automated analysis of video recordings of general movements in infants during assessments.
Comparison of automated predictions with traditional assessment methods.
Evaluation of accuracy and reliability against established benchmarks.
Automated system achieved a prediction accuracy of 85% for identifying infants at risk of cerebral palsy (p<0.01).
Significant reduction in assessment time by 30% compared to manual assessment.
Identified challenges in automation, including variability in infant movements.

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Evaluates automated General Movements Assessment for predicting cerebral palsy in infants, highlighting its potential and challenges.

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Spittle et al. (2026) studied this question.

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