Cargando…
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...
Autores principales: | , , , , , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1783497596939010048 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7019773 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT ihlenespenaf machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT støenragnhild machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT boswelllynn machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT deregnierrayeann machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT fjørtofttoril machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT gaeblerspiradeborah machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT laboricathrine machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT loenneckenmariannec machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT msallmichaele machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT moinichenunni machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT peytoncolleen machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT schreibermichaeld machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT silbergingere machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT songstadnilst machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT vagenrandit machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT øberggunnk machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy AT addelars machinelearningofinfantspontaneousmovementsfortheearlypredictionofcerebralpalsyamultisitecohortstudy |