Artificial intelligence to improve efficiency of administration of gross motor function assessment in children with cerebral palsy

Ibrahim Duran; Christina Stark; Ahmet Saglam; Alexandra Semmelweis; Heidrun Lioba Wunram; Karoline Spiess; Eckhard Schoenau

doi:10.1111/dmcn.15010

Artificial intelligence to improve efficiency of administration of gross motor function assessment in children with cerebral palsy

Dev Med Child Neurol. 2022 Feb;64(2):228-234. doi: 10.1111/dmcn.15010. Epub 2021 Aug 13.

Authors

Ibrahim Duran¹, Christina Stark², Ahmet Saglam³, Alexandra Semmelweis¹, Heidrun Lioba Wunram⁴, Karoline Spiess¹, Eckhard Schoenau^{1

5}

Affiliations

¹ Center of Prevention and Rehabilitation, Medical Faculty and University Hospital, University of Cologne, Cologne.
² Department of Neurology, Medical Faculty and University Hospital, University of Cologne, Cologne, Germany.
³ Merzifon Vocational School Computer Technologies Department, University of Amasya, Amasya, Turkey.
⁴ Department of Psychiatry, Psychosomatics and Psychotherapy for Children and Adolescents, Medical Faculty and University Hospital, University of Cologne, Cologne, Germany.
⁵ Department of Pediatrics, Medical Faculty and University Hospital, University of Cologne, Cologne, Germany.

PMID: 34387869
DOI: 10.1111/dmcn.15010

Abstract

Aim: To create a reduced version of the 66-item Gross Motor Function Measure (rGMFM-66) using innovative artificial intelligence methods to improve efficiency of administration of the GMFM-66.

Method: This study was undertaken using information from an existing data set of children with cerebral palsy participating in a rehabilitation programme. Different self-learning approaches (random forest, support vector machine [SVM], and artificial neural network) were evaluated to estimate the GMFM-66 score with the fewest possible test items. Test agreements were evaluated (among other statistics) by intraclass correlation coefficients (ICCs).

Results: Overall, 1217 GMFM-66 assessments (509 females, mean age 8y 10mo [SD 3y 9mo]) at a single time and 187 GMFM-66 assessments and reassessments (80 females, mean age 8y 5mo [SD 3y 10mo]) after 1 year were evaluated. The model with SVM predicted the GMFM-66 scores most accurately. The ICCs of the rGMFM-66 and the full GMFM-66 were 0.997 (95% confidence interval [CI] 0.996-0.997) at a single time and 0.993 (95% CI 0.993-0.995) for the evaluation of the change over time.

Interpretation: The study shows that the efficiency of the full GMFM-66 assessment can be increased by using machine learning (self-learning algorithms). The presented rGMFM-66 score showed an excellent agreement with the full GMFM-66 score when applied to a single assessment and when evaluating the change over time.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Artificial Intelligence*
Cerebral Palsy / diagnosis*
Cerebral Palsy / physiopathology*
Child
Child, Preschool
Female
Humans
Machine Learning
Male
Motor Skills / physiology*
Neural Networks, Computer
Prospective Studies
Retrospective Studies
Severity of Illness Index*
Support Vector Machine