NCT07147478

Video Analysis and Artificial Intelligence for the Analysis of Upper Limb Movement in Children

Video Analysis and Artificial Intelligence for the Analysis of Upper Limb Movement in Children. Validation of a Technique in a Pediatric Population Aged 6 to 17 Years

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 150 (estimated)
• Sponsor: Assistance Publique - Hôpitaux de Paris · Academic / Other
• Sex: All
• Age: 6 Years – 17 Years
• Healthy volunteers: Accepted

Summary

The principle of the study is to compare the data obtained using a shoulder movement analysis software with those obtained during a traditional clinical examination, that is, using a goniometer and the modified Mallet classification

Detailed description

The children are recorded performing 3 sets of shoulder movements (abduction, adduction, flexion, extension, external rotation 1, external rotation 2, internal rotation 2), first on the left and then on the right, at maximum active range of motion, chosen active range of motion, and maximum passive range of motion. The recordings are made by an RGB-D camera connected to a software (ShoulderLoc from B-com) equipped with artificial intelligence that, after image processing, determines the joint range angle of the shoulder for the given movement. This value is compared to the visual estimation of the examiner and its measurement using a goniometer. The hand-mouth, hand-neck, and internal rotation 1 movements are also performed and compared with the data from the modified Mallet classification.

Conditions

• Clinical Examination of the Shoulder
• Video-assisted Clinical Examination

Timeline

• Start date: 2025-09-01
• Primary completion: 2026-06-01
• Completion: 2026-12-01
• First posted: 2025-08-29
• Last updated: 2025-08-29

Locations

1 site across 1 country: France