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Feasibility of Vision-Language Models for Detecting Physical Assistance as an Observable Criterion Defining GMFCS Levels in Children Aged 2 to 6 Years With Cerebral Palsy
Sponsor: Samsung Medical Center
Summary
The GMFCS sorts children with cerebral palsy into five levels of gross motor function. What mainly separates one level from the next is two things an observer can see: whether another person has to physically help the child move, and whether the child has to bear weight on something external, such as a walker, a support stand, or furniture. Scoring these takes an experienced clinician, and it is hardest between the ages of 2 and 6. This study asks a narrow question. Can a vision-language model, meaning an artificial-intelligence model that looks at images and answers questions about them, detect those two things from short video clips of a child moving? Twenty-six children with cerebral palsy aged 2 to 6 years were filmed at two hospitals. Each child performed everyday movements, including walking, crawling, rolling sideways, rising from the floor to standing, and lowering back to the floor, and up to three cameras recorded every attempt at the same time. Sixteen still frames were taken from each recorded attempt, always sixteen, and shown to the model without the child's identity, the name of the movement, or the child's GMFCS level. The model answers two yes-or-no questions and nothing else: did an adult touch the child, and did the child bear weight on an external object. A fixed rule, not the model, turns those answers into one determination per attempt, namely whether the child performed the movement unaided. These determinations are compared with what a human annotator recorded for the same clips while blind to clinical information. All rating is done with open-weight models running on hardware the investigators control, and every reported figure is tied to the specific model version that produced it. The study does not assign a GMFCS level, and it is not built to. It tests whether the two observations the GMFCS itself relies on can be read from video reliably. If they can, they can be placed in front of a clinician as evidence, which is where later work on supporting GMFCS assessment would start.
Official title: Feasibility of a Self-Hosted Vision-Language Model for Detecting Caregiver Contact and External Support, the Observable Criteria That Define GMFCS Levels, From Multi-View Movement Video in Children Aged 2 to 6 Years With Cerebral Palsy: A Two-Center Diagnostic Accuracy Study
Key Details
Gender
All
Age Range
2 Years - 6 Years
Study Type
OBSERVATIONAL
Enrollment
26
Start Date
2025-05-30
Completion Date
2026-12
Last Updated
2026-09-17
Healthy Volunteers
No
Conditions
Interventions
Vision-language model assessment of movement video
Standardized multi-view video recording of five prescribed gross motor tasks, followed by automated reading of the recordings. From each recorded attempt, 16 frames are extracted, 8 from each of two camera views, and presented to an open-weight vision-language model. The model answers two binary questions: whether an adult touched the child during the attempt, and whether the child bore load through an external object. A fixed rule combines the two answers into a determination of whether the attempt was performed unaided. The model is given no participant identifier, no movement name and no GMFCS level, and is never asked for a clinical grade. All models run on hardware controlled by the investigators.
Locations (2)
Asan Medical Center
Seoul, South Korea
Samsung Medical Center
Seoul, South Korea