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

Espen A F Ihlen¹, Ragnhild Støen^{2

3}, Lynn Boswell⁴, Raye-Ann de Regnier^{4

5}, Toril Fjørtoft^{3

6}, Deborah Gaebler-Spira^{5

7}, Cathrine Labori⁸, Marianne C Loennecken⁹, Michael E Msall^{10

11}, Unn I Möinichen⁹, Colleen Peyton^{5

12}, Michael D Schreiber¹⁰, Inger E Silberg⁹, Nils T Songstad¹³, Randi T Vågen⁶, Gunn K Øberg^{8

14}, Lars Adde^{3

6}

Affiliations

¹ Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway.
² Department of Neonatology, St. Olavs hospital, Trondheim University Hospital, 7006 Trondheim, Norway.
³ 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.
⁵ Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
⁶ Clinic of Clinical Services, St. Olavs hospital, Trondheim University Hospital, 7006 Trondheim, Norway.
⁷ Shirley Ryan AbilityLab, Chicago, IL 60611, USA.
⁸ Department of Clinical Therapeutic Services, University Hospital of North Norway, 9038 Tromsø, Norway.
⁹ Department of Pediatrics, Division of Paediatric and Adolescent Medicine, Oslo University Hospital, 0372 Oslo, Norway.
¹⁰ University of Chicago Medicine, Comer Children's Hospital, Chicago, IL, Section of Developmental and Behavioral Pediatrics, Chicago, IL.60637, USA.
¹¹ University of Chicago, Kennedy Research Center on Intellectual and Neurodevelopmental Disabilities, Chicago, IL 60637, USA.
¹² Department of Pediatrics, Comer Children's Hospital, Department of Physical Therapy and Human Movement Science, Chicago, IL 60637, USA.
¹³ Department of Pediatrics and Adolescent Medicine, University Hospital of North Norway, 9038 Tromsø, Norway.
¹⁴ Department of Health and Care Sciences, Faculty of Health Sciences, UiT- The Arctic University of Norway, 9019 Tromsø, Norway.

PMID: 31861380
PMCID: PMC7019773
DOI: 10.3390/jcm9010005

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

Espen A F Ihlen et al. J Clin Med. 2019.

. 2019 Dec 18;9(1):5.

doi: 10.3390/jcm9010005.

Authors

Affiliations

¹ Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway.
² Department of Neonatology, St. Olavs hospital, Trondheim University Hospital, 7006 Trondheim, Norway.
³ 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.
⁵ Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
⁶ Clinic of Clinical Services, St. Olavs hospital, Trondheim University Hospital, 7006 Trondheim, Norway.
⁷ Shirley Ryan AbilityLab, Chicago, IL 60611, USA.
⁸ Department of Clinical Therapeutic Services, University Hospital of North Norway, 9038 Tromsø, Norway.
⁹ Department of Pediatrics, Division of Paediatric and Adolescent Medicine, Oslo University Hospital, 0372 Oslo, Norway.
¹⁰ University of Chicago Medicine, Comer Children's Hospital, Chicago, IL, Section of Developmental and Behavioral Pediatrics, Chicago, IL.60637, USA.
¹¹ University of Chicago, Kennedy Research Center on Intellectual and Neurodevelopmental Disabilities, Chicago, IL 60637, USA.
¹² Department of Pediatrics, Comer Children's Hospital, Department of Physical Therapy and Human Movement Science, Chicago, IL 60637, USA.
¹³ Department of Pediatrics and Adolescent Medicine, University Hospital of North Norway, 9038 Tromsø, Norway.
¹⁴ Department of Health and Care Sciences, Faculty of Health Sciences, UiT- The Arctic University of Norway, 9019 Tromsø, Norway.

PMID: 31861380
PMCID: PMC7019773
DOI: 10.3390/jcm9010005

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.

Keywords: cerebral palsy; general movement assessment; machine learning; premature infants.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest. Nor the funders had any role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish results. Colleen Peyton is a member of the Prechtl General Movement Trust speaker’s bureau.

Figures

**Figure A1**
Schematic illustration of the cross-validation (CV) procedure used to validate the CIMA model.

**Figure A2**
Receiver operating characteristic (ROC) curve of the proportion of periods with CP risk-related movements (red graph) compared to the ROC curve of the standard deviation of the center of motion (C_SD) (blue graph). The horizontal and vertical dashed lines indicate points on the red ROC curve for the different decision thresholds represented in Table A2.

**Figure 1**
Flow-chart of exclusion of video recordings for the development and testing of the Computer-based Infant Movement Assessment (CIMA) model.

**Figure 2**
Steps of the CIMA model. First, infant movements are detected by motion tracking of six body parts (head, trunk, arms, and legs) in the video. Second, features for the movement frequencies, amplitude, and covariation of the different body parts are extracted from the body part movement trajectories and used in the CP prediction model. The CP prediction model identifies 5 second periods with CP risk-related movements. Finally, the proportion (%) of periods with CP risk-related movements typically found in infants with CP is summarized and communicated as a CP risk indicator.

**Figure 3**
Each bar represents the proportion (%) of periods with CP risk-related movements represented in the video recordings of each of the 377 infants. The bars are centered around the decision threshold of 50% (horizontal line) for increased risk of CP. The red bars are from infants with confirmed CP diagnosis, whereas the blue bars represent the infants with a confirmed non-CP diagnosis.

**Figure 4**
Boxplot of the proportion of periods with CP risk-related movements assessed by the CIMA model (y-axis) and temporal organization of FMs assessed by observational GMA (x-axis) according to CP outcome. The red line indicates the median and blue box the interquartile range. The whiskers in dashed lines are 1.5 times the interquartile range and cover 99.3% of the data if normally distributed. Outliers are marked as red crosses. The horizontal dashed line represents a decision threshold of 50% for the CIMA model. FM− = absent FM; FM−/+ = sporadic FM; FM+ = intermittent FM; FM++ = continual FM; FMa = FM with exaggerated speed and amplitude.

See this image and copyright information in PMC

References

1. Rosenbaum P., Paneth N., Leviton A., Goldstein M., Bax M., Damiano D., Dan B., Jacobsson B. A report: The definition and classification of cerebral palsy. Dev. Med. Child Neurol. Suppl. 2007;109:8–14. - PubMed
1. Oskoui M., Coutinho F., Dykeman J., Jette N., Pringsheim T. An update on the prevalence of cerebral palsy: A systematic review and meta-analysis. Dev. Med. Child Neurol. 2013;55:509–519. doi: 10.1111/dmcn.12080. - DOI - PubMed
1. Novak I., Morgan C., Adde L., Blackman J., Boyd R.N., Brunstrom-Hernandez J., Cioni G., Damiano D., Darrah J., Eliasson A.C., et al. Early, accurate diagnosis and early intervention in cerebral palsy: Advances in Diagnosis and Treatment. JAMA Pediatr. 2017;171:897–907. doi: 10.1001/jamapediatrics.2017.1689. - DOI - PMC - PubMed
1. Guttmann K., Flibotte J., DeMauro S.B. Parental Perspectives on Diagnosis and Prognosis of Neonatal Intensive Care Unit Graduates with Cerebral Palsy. J. Pediatr. 2018;203:156–162. doi: 10.1016/j.jpeds.2018.07.089. - DOI - PubMed
1. Baird G., McConachie H., Scrutton D. Parents’ perceptions of disclosure of the diagnosis of cerebral palsy. Arch. Dis. Child. 2000;83:475–480. doi: 10.1136/adc.83.6.475. - DOI - PMC - PubMed

Grants and funding

SO: 90056100/The Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology, Trondheim, Norway

LinkOut - more resources

Full Text Sources
- Europe PubMed Central

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Grants and funding

LinkOut - more resources

Full Text Sources