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dc.contributor.authorIhlen, Espen Alexander F.
dc.contributor.authorStøen, Ragnhild
dc.contributor.authorBoswell, Lynn
dc.contributor.authorde-Regnier, Raye-Ann
dc.contributor.authorFjørtoft, Toril Larsson
dc.contributor.authorGaebler-Spira, Deborah
dc.contributor.authorLabori, Cathrine
dc.contributor.authorLoennecken, Marianne
dc.contributor.authorMsall, Michael
dc.contributor.authorMøinichen, Unn Inger
dc.contributor.authorPeyton, Colleen
dc.contributor.authorSchreiber, Michael
dc.contributor.authorSilberg, Inger Elisabeth
dc.contributor.authorSongstad, Nils Thomas
dc.contributor.authorVågen, Randi
dc.contributor.authorØberg, Gunn Kristin
dc.contributor.authorAdde, Lars
dc.date.accessioned2020-03-05T11:45:34Z
dc.date.available2020-03-05T11:45:34Z
dc.date.issued2019-12-18
dc.description.abstract<i>Background</i>: 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.<p><p> <i>Methods</i>: 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.<p><p> <i>Results</i>: 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%, <i>p</i> = 0.02) compared with those with ambulatory CP (median: 72.7%).<p><p> <i>Conclusion</i>: The CIMA model may be a clinically feasible alternative to observational GMA.en_US
dc.identifier.citationIhlen EAF, Støen R, Boswell L, de-Regnier R, Fjørtoft TLF, Gaebler-spira D, Labori C, Loennecken M, Msall M, Møinicken Ui, Peyton C, Schreiber M, Silberg IE, Songstad NT, Vaagen R, Øberg gk, Adde L. Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study. Journal of Clinical Medicine. 2019en_US
dc.identifier.cristinIDFRIDAID 1767090
dc.identifier.doi10.3390/jcm9010005
dc.identifier.issn2077-0383
dc.identifier.urihttps://hdl.handle.net/10037/17641
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalJournal of Clinical Medicine
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2019 The Author(s)en_US
dc.subjectVDP::Medical disciplines: 700en_US
dc.subjectVDP::Medisinske Fag: 700en_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Teknologi: 500en_US
dc.titleMachine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Studyen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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