Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study

Espen A F Ihlen, Ragnhild Støen, Lynn Boswell, Raye-Ann de Regnier, Toril Fjørtoft, Deborah Gaebler-Spira, Cathrine Labori, Marianne C Loennecken, Michael E Msall, Unn I Möinichen, Colleen Peyton, Michael D Schreiber, Inger E Silberg, Nils T Songstad, Randi T Vågen, Gunn K Øberg, Lars Adde

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
JournalJournal of Clinical Medicine
Volume9
Issue number1
DOIs
StatePublished - Dec 18 2019

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    Ihlen, E. A. F., Støen, R., Boswell, L., Regnier, R-A. D., Fjørtoft, T., Gaebler-Spira, D., Labori, C., Loennecken, M. C., Msall, M. E., Möinichen, U. I., Peyton, C., Schreiber, M. D., Silberg, I. E., Songstad, N. T., Vågen, R. T., Øberg, G. K., & Adde, L. (2019). Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study. Journal of Clinical Medicine, 9(1). https://doi.org/10.3390/jcm9010005