Background: Early identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the Computer-based Infant Movement Assessment (CIMA) model, for clinically feasible early CP prediction based on infant video recordings. Methods: The CIMA model was designed to assess the proportion (%) of CP risk-related movements using a time–frequency decomposition of the movement trajectories of the infant’s body parts. The CIMA model was developed and tested on video recordings from a cohort of 377 high-risk infants at 9–15 weeks corrected age to predict CP status and motor function (ambulatory vs. non-ambulatory) at mean 3.7 years age. The performance of the model was compared with results of the general movement assessment (GMA) and neonatal imaging. Results: The CIMA model had sensitivity (92.7%) and specificity (81.6%), which was comparable to observational GMA or neonatal cerebral imaging for the prediction of CP. Infants later found to have non-ambulatory CP had significantly more CP risk-related movements (median: 92.8%, p = 0.02) compared with those with ambulatory CP (median: 72.7%). Conclusion: The CIMA model may be a clinically feasible alternative to observational GMA.

Cerebral palsy (CP) is the most common physical disability in children, producing functional limitation and co-occurring impairments¹ (eg, pain, musculoskeletal deformities, seizures, and communication and sleep disorders) because of injury to the developing brain.² Cerebral palsy is typically diagnosed between ages 12 and 24 months, and milder forms of CP may be diagnosed even later in childhood.^3,4 Early identification of infants with a high risk of CP is important to provide targeted follow-up and interventions during infancy when neuroplasticity is high,^5,6 improve access to community services to minimize complications,⁷ and reassure parents of infants at high risk if their children are unlikely to develop CP.⁸