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.
journal_name
J Clin Medjournal_title
Journal of clinical medicineauthors
Ihlen EAF,Støen R,Boswell L,Regnier RA,Fjørtoft T,Gaebler-Spira D,Labori C,Loennecken MC,Msall ME,Möinichen UI,Peyton C,Schreiber MD,Silberg IE,Songstad NT,Vågen RT,Øberg GK,Adde Ldoi
10.3390/jcm9010005subject
Has Abstractpub_date
2019-12-18 00:00:00issue
1issn
2077-0383pii
jcm9010005journal_volume
9pub_type
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