AIMAS, Developmental Coordination Disorder
Conditions
Keywords
Schoolchildren, Assessment, Artificial Intelligence, Motor, Developmental coordination disorder
Brief summary
The purpose of this study is to develop an AI-based automated motor function assessment system (AIMAS) to improve early identification of developmental coordination disorder (DCD) in school-age children. The main hypothesis for this study is: Integrating AI into motor skill assessments will enhance the reliability, validity, efficiency, and accuracy of evaluating motor performance in children aged 6 to 12.
Interventions
None listed
Sponsors
Chang Gung Memorial Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
6 Years to 12 Years
Healthy volunteers
No
Inclusion criteria
* Children aged 6 to 12 years. * For DCD group: formal diagnosis of Developmental Coordination Disorder (DCD). * For typically developing group: no disabilities or developmental delays.
Exclusion criteria
* Acute illnesses (e.g., pneumonia, upper gastrointestinal hemorrhage). * Significant developmental delays or disabilities. * Genetic diseases or disorders. * Neurological disorders or injuries.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Motor Function Assessment Score | Baseline | AI-based Motor Function Test (Higher scores indicate better motor performance and lower risk of Developmental Coordination Disorder (DCD).) |
| DCD Risk Classification | Baseline | AIMAS System Classification (System's ability to identify children at risk of DCD, compared to clinical diagnosis.) |
Secondary
| Measure | Time frame |
|---|---|
| The Bruininks-Oseretsky Test of Motor Proficiency, Second Edition (BOT-2) | baseline |
Countries
Taiwan
Outcome results
None listed