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

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 predictio...

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Bibliographic Details
Published in:Journal of clinical medicine 2019-12, Vol.9 (1), p.5
Main Authors: Ihlen, Espen A F, Støen, Ragnhild, Boswell, Lynn, Regnier, Raye-Ann de, Fjørtoft, Toril, Gaebler-Spira, Deborah, Labori, Cathrine, Loennecken, Marianne C, Msall, Michael E, Möinichen, Unn I, Peyton, Colleen, Schreiber, Michael D, Silberg, Inger E, Songstad, Nils T, Vågen, Randi T, Øberg, Gunn K, Adde, Lars
Format: Article
Language:English
Subjects:
Age
Babies
Cerebral palsy
Clinical medicine
Cohort analysis
Machine learning
Medical disciplines: 700
Medisinske Fag: 700
Risk assessment
Technology: 500
Teknologi: 500
Validation studies
Validity
VDP
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Summary: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. 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. 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%, = 0.02) compared with those with ambulatory CP (median: 72.7%). The CIMA model may be a clinically feasible alternative to observational GMA.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm9010005