Shoulder
Conditions
Keywords
mHealth, Precision Medicine, Computer Vision, Skeletal Tracking, Deep Learning
Brief summary
Shoulder pain is one of the most prevalent musculoskeletal conditions. Evidence-based medicine has identified physical therapy as the most effective intervention for managing shoulder disorders. To ensure accurate diagnosis and effective treatment planning, a comprehensive evaluation that integrates various clinical findings is essential. Without timely and accurate diagnosis and intervention, shoulder pain may recur and fail to improve, limiting therapeutic outcomes. With technological advancements, the application of mobile devices and artificial intelligence (AI) in clinical settings has become increasingly widespread. Motion capture technologies integrated into mobile platforms offer emerging solutions for clinical challenges. If clinicians are equipped with an intelligent system for shoulder assessment and intervention-one that includes image-based quantitative assessment tools, evidence-based clinical guidelines and data repositories, and home-based rehabilitation support-it may enhance diagnostic precision, increase clinical efficiency, and improve patient adherence to home exercise programs. The aim of this study is to develop a smart technology-assisted assessment system for orthopedic physical therapy of the shoulder joint and to validate its reliability and validity. This system will provide clinicians with objective, data-driven evaluation results. In future development, it will also offer support in treatment goal setting, intervention planning, and home-based exercise guidance. The proposed intelligent system is expected to serve as an evidence-based clinical aid, enhancing both the precision and efficiency of physical therapy interventions.
Interventions
None listed
Sponsors
National Cheng-Kung University Hospital
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
20 Years to 65 Years
Healthy volunteers
Yes
Inclusion criteria
for group 1: * BMI (body mass index) between 18.5-24.9 * self-perceived good physical condition * no history of shoulder orthopedic disease or nerve damage Inclusion Criteria for group 2: * Non-acute shoulder orthopedic diseases diagnosed by orthopedic physicians or assessed by physical therapists * may include but are not limited to the following diseases: frozen shoulder, shoulder compression syndrome, shoulder rotator injury, shoulder instability, etc.
Exclusion criteria
* Any neurological disease that may cause pain * a history of related surgery in the past six months * acute inflammation of the shoulder joint * open wounds in the shoulder joint area * Principle Investor's teaching students, laboratory assistants
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Range of Motion | Total 3 times. First trail at day1 (after enrollment) Second trail at 1hour after first trial Third trail is at day2 | Shoulder Range of Motion |
Countries
Taiwan
Outcome results
None listed