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

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...

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Autores principales: Ihlen, Espen A. F., Støen, Ragnhild, Boswell, Lynn, de Regnier, Raye-Ann, 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7019773/
https://www.ncbi.nlm.nih.gov/pubmed/31861380
http://dx.doi.org/10.3390/jcm9010005
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author Ihlen, Espen A. F.
Støen, Ragnhild
Boswell, Lynn
de Regnier, Raye-Ann
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
author_facet Ihlen, Espen A. F.
Støen, Ragnhild
Boswell, Lynn
de Regnier, Raye-Ann
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
author_sort Ihlen, Espen A. F.
collection PubMed
description 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.
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spelling pubmed-70197732020-03-09 Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study Ihlen, Espen A. F. Støen, Ragnhild Boswell, Lynn de Regnier, Raye-Ann 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 J Clin Med Article 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. MDPI 2019-12-18 /pmc/articles/PMC7019773/ /pubmed/31861380 http://dx.doi.org/10.3390/jcm9010005 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ihlen, Espen A. F.
Støen, Ragnhild
Boswell, Lynn
de Regnier, Raye-Ann
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
Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study
title Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study
title_full Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study
title_fullStr Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study
title_full_unstemmed Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study
title_short Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study
title_sort machine learning of infant spontaneous movements for the early prediction of cerebral palsy: a multi-site cohort study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7019773/
https://www.ncbi.nlm.nih.gov/pubmed/31861380
http://dx.doi.org/10.3390/jcm9010005
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