Clinical Examination of the Shoulder, Video-assisted Clinical Examination
Conditions
Keywords
Clinical Examination of the Shoulder, Video-assisted clinical examination, Artificial intelligence, ShoulderLoc, Goniometer
Brief 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.
Interventions
None listed
Sponsors
Assistance Publique - Hôpitaux de Paris
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
6 Years to 17 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Comparison of a video-assisted clinical examination method with commonly used clinical practices | 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
| Measure | Time frame | Description |
|---|---|---|
| Exam duration based on age | 1 day | Validate the methodology and protocol for acquiring video movements in children |
| Technical difficulties. | 1 day | Validate the methodology and protocol for acquiring video movements in children |
| Measurement of optimal acquisition distances for video quality, | 1 day | Validate the methodology and protocol for acquiring video movements in children |
| Compare measurements obtained in active and passive motion for the same movement, through video measurement and manual (goniometer) measurement, to simple visual estimation measurements. | 1 day | Comparison of the average angles obtained in active and passive motion using different methods, compared to visual assessment. |
| Study the satisfaction of the contribution of video tools in daily clinical practice | 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). |
| Obtain objective, quantified data on pure and combined shoulder movements | 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. |
Countries
France
Contacts
Primary ContactManon BACHY-RAZZOUK, MCU-PH
Backup ContactEstelle ALONSO, Intern
Outcome results
None listed