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NCT Number: NCT07147478

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

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

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Key information

Age range

6 year–17 year

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age between 6 and 17 years old at the time of inclusion
  • No neurological pathology
  • No history of upper limb surgery
  • No upper limb trauma above the hand in the 6 months preceding the examination
  • Ability to stand for a minimum of 2 minutes
  • Consent from the child, both parents, and/or legal representatives for participation in a filmed clinical examination.

Exclusion criteria

  • Inability to understand the different movements requested.

Treatment and study plan

Primary outcomes

  1. Comparison of a video-assisted clinical examination method with commonly used clinical practices

    Time frame: 1 day

    Perform 2 sets of 7 to 10 shoulder movements, both active and passive, measure mobility angles using a goniometer and the Mallet classification, as well as the ShoulderLoc software and its artificial intelligence program. Compare the averages obtained for each movement using both methods and compare them using an intraclass correlation coefficient (ICC).

Secondary outcomes

  1. Measurement of optimal acquisition distances for video quality,

    Time frame: 1 day

    Validate the methodology and protocol for acquiring video movements in children

  2. Exam duration based on age

    Time frame: 1 day

    Validate the methodology and protocol for acquiring video movements in children

  3. Technical difficulties.

    Time frame: 1 day

    Validate the methodology and protocol for acquiring video movements in children

  4. Obtain objective, quantified data on pure and combined shoulder movements

    Time frame: 1 day

    Description of results obtained by video recording a series of successive movements and analyzing them with artificial intelligence that allows for angle calculations.

  5. Compare measurements obtained in active and passive motion for the same movement, through video measurement and manual (goniometer) measurement, to simple visual estimation measurements.

    Time frame: 1 day

    Comparison of the average angles obtained in active and passive motion using different methods, compared to visual assessment.

  6. Study the satisfaction of the contribution of video tools in daily clinical practice

    Time frame: 1 day

    Data collection to assess feasibility in daily clinical practice (subgroup studies concerning equipment usage parameters to propose a protocol adapted to children's age).

Study contacts

Contact information is provided by the study sponsor or research team.

Estelle ALONSO, Intern

CONTACT

[email protected]

Manon BACHY-RAZZOUK, MCU-PH

CONTACT

[email protected]

00 33 1 44 73 69 37

Sponsors and collaborators

Lead sponsor

Assistance Publique - Hôpitaux de Paris

Other

Registry information

Official study title

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

Acronym: AIME

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Aug 29, 2025
Registry last updated
Aug 29, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.