AI Article Synopsis
- The study aimed to shorten the 66-item Gross Motor Function Measure (GMFM-66) using artificial intelligence to make it easier to administer.
- Researchers analyzed data from children with cerebral palsy to assess the GMFM-66 scores with fewer items, testing various self-learning approaches like random forest and support vector machines.
- The results showed that the support vector machine model predicted GMFM-66 scores most accurately, with very high agreement between the new reduced version (rGMFM-66) and the full version, indicating improved efficiency in assessments.
Article 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.
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Article Synopsis
- The study aimed to shorten the 66-item Gross Motor Function Measure (GMFM-66) using artificial intelligence to make it easier to administer.
- Researchers analyzed data from children with cerebral palsy to assess the GMFM-66 scores with fewer items, testing various self-learning approaches like random forest and support vector machines.
- The results showed that the support vector machine model predicted GMFM-66 scores most accurately, with very high agreement between the new reduced version (rGMFM-66) and the full version, indicating improved efficiency in assessments.
How does the intensity of physical therapy affect the Gross Motor Function Measure (GMFM-66) total score in children with cerebral palsy? A systematic review protocol.
BMJ Open
July 2020
Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona, USA.
Introduction: Intensive physical therapy (PT) interventions administered to children with cerebral palsy (CP) have received a significant amount of attention in published literature. However, there is considerable variability in therapy intensity among studies and notable lack of information on optimal intervention dosing. This makes it difficult for clinicians to use evidence to inform practice.
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