Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort
Espen A F Ihlen1, Ragnhild Støen2,3, Lynn Boswell4
1Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway.
Insights
A new machine learning model, the Computer-based Infant Movement Assessment (CIMA), can predict cerebral palsy (CP) in infants from video recordings. This tool offers early detection for timely intervention.
Area of Science:
- Pediatric neurology
- Machine learning in healthcare
- Developmental pediatrics
Background:
- Early identification of cerebral palsy (CP) in infancy is crucial for timely therapeutic interventions.
- Current diagnostic methods for CP can be improved for earlier and more accurate prediction.
- Infant movement analysis holds potential for non-invasive CP assessment.
Purpose of the Study:
- To introduce a novel machine learning model, the Computer-based Infant Movement Assessment (CIMA), for early CP prediction.
- To evaluate the clinical feasibility of the CIMA model using infant video recordings.
- To compare the predictive performance of CIMA with existing methods like General Movement Assessment (GMA) and neonatal imaging.
Main Methods:
- The CIMA model analyzes movement trajectories of infant body parts using time-frequency decomposition.
- The model quantifies the proportion of CP risk-related movements observed in video recordings.
- Development and testing involved 377 high-risk infants (9-15 weeks corrected age), with CP status assessed at 3.7 years.
Main Results:
- The CIMA model demonstrated high sensitivity (92.7%) and specificity (81.6%) in predicting CP.
- Its performance was comparable to observational GMA and neonatal imaging.
- Infants with non-ambulatory CP exhibited significantly higher proportions of CP risk-related movements (median 92.8%) than those with ambulatory CP (median 72.7%).
Conclusions:
- The CIMA model presents a clinically feasible alternative for early CP detection.
- It offers a promising tool for objective and quantitative assessment of infant movements.
- The model's ability to differentiate between ambulatory and non-ambulatory CP warrants further investigation.
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.
