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The Pooled Diagnostic Accuracy of Neuroimaging, General Movements, and Neurological Examination for Diagnosing Cerebral Palsy Early in High-Risk Infants: A Case Control Study
Article

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

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Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway
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Department of Neonatology, St. Olavs Hospital, Trondheim University Hospital, 7006 Trondheim, Norway
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Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway
4
Ann and Robert H Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
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Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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Clinic of Clinical Services, St. Olavs Hospital, Trondheim University Hospital, 7006 Trondheim, Norway
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Shirley Ryan AbilityLab, Chicago, IL 60611, USA
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Department of Clinical Therapeutic Services, University Hospital of North Norway, 9038 Tromsø, Norway
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Department of Pediatrics, Division of Paediatric and Adolescent Medicine, Oslo University Hospital, 0372 Oslo, Norway
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University of Chicago Medicine, Comer Children’s Hospital, Section of Developmental and Behavioral Pediatrics, Chicago, IL 60637, USA
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University of Chicago, Kennedy Research Center on Intellectual and Neurodevelopmental Disabilities, Chicago, IL 60637, USA
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Department of Pediatrics, Comer Children’s Hospital, Department of Physical Therapy and Human Movement Science, Chicago, IL 60637, USA
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Department of Pediatrics and Adolescent Medicine, University Hospital of North Norway, 9038 Tromsø, Norway
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Department of Health and Care Sciences, Faculty of Health Sciences, UiT- The Arctic University of Norway, 9019 Tromsø, Norway
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(1), 5; https://doi.org/10.3390/jcm9010005
Received: 22 October 2019 / Revised: 29 November 2019 / Accepted: 16 December 2019 / Published: 18 December 2019
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. View Full-Text
Keywords: cerebral palsy; premature infants; general movement assessment; machine learning cerebral palsy; premature infants; general movement assessment; machine learning
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MDPI and ACS Style

Ihlen, E.A.F.; Støen, R.; Boswell, L.; de Regnier, R.-A.; Fjørtoft, T.; Gaebler-Spira, D.; Labori, C.; Loennecken, M.C.; Msall, M.E.; Möinichen, U.I.; Peyton, C.; Schreiber, M.D.; Silberg, I.E.; Songstad, N.T.; Vågen, R.T.; Øberg, G.K.; Adde, L. Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study. J. Clin. Med. 2020, 9, 5. https://doi.org/10.3390/jcm9010005

AMA Style

Ihlen EAF, Støen R, Boswell L, de Regnier R-A, 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 L. Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study. Journal of Clinical Medicine. 2020; 9(1):5. https://doi.org/10.3390/jcm9010005

Chicago/Turabian Style

Ihlen, Espen A.F., Ragnhild Støen, Lynn Boswell, Raye-Ann de Regnier, Toril Fjørtoft, Deborah Gaebler-Spira, Cathrine Labori, Marianne C. Loennecken, Michael E. Msall, Unn I. Möinichen, Colleen Peyton, Michael D. Schreiber, Inger E. Silberg, Nils T. Songstad, Randi T. Vågen, Gunn K. Øberg, and Lars Adde. 2020. "Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study" Journal of Clinical Medicine 9, no. 1: 5. https://doi.org/10.3390/jcm9010005

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