Efficiency and Validity of the AI-Based rGMFM-66 in Assessing Gross Motor Function in Children with Cerebral Palsy

Assessing gross motor function in children with cerebral palsy (CP) is critical for monitoring progress and planning interventions. While the Gross Motor Function Measurement (GMFM-66) is an established tool, its application can be time-intensive. This study aimed to evaluate the efficiency and validity of a reduced version, the reduced Gross Motor Function Measurement (rGMFM-66). In a single-center study, 52 children with cerebral palsy were assessed using both the GMFM-66 and rGMFM-66 within a 1 to 2-day interval. The rGMFM-66 utilized an artificial intelligence algorithm to streamline the assessment process. Assessment duration and scores were compared, and agreement between the two measures was evaluated using intraclass correlation coefficients (ICC) and Bland–Altman analysis. The rGMFM-66 significantly reduced assessment time (mean 17.2 vs. 38.5 min; p < 0.001; effect size = 1.9). It showed excellent agreement with the GMFM-66 (ICC = 0.970; 95% CI: 0.942–0.983). Scores were slightly higher for the rGMFM-66 (mean 52.2 vs. 50.7; p = 0.008), which might be influenced by factors such as test familiarity or order of administration. In conclusion, the rGMFM-66 is a valid and time-efficient alternative for evaluating gross motor abilities in children with CP. Its application may support more efficient assessments in clinical settings without compromising accuracy. Further external validation in larger and more diverse populations is recommended to confirm these findings.