Comparison of an Artificial Intelligence-Assisted Rehabilitation Program for Shoulder Musculoskeletal Disorders and the Clinical Decision Making of Therapists
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Hanyun Hsiao, master
- Phone Number: 1624 +88622490088
- Email: 10252@s.tmu.edu.tw
Study Locations
-
-
-
New Taipei City, Taiwan, 235
- Recruiting
- Shuang Ho Hospital
-
Contact:
- Hanyun Hsiao, master
- Phone Number: 1624 +88622490088
- Email: 10252@s.tmu.edu.tw
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- The International Classification of Diseases, 10th revision (ICD-10) codes were selected before the study started and included the ICD-10 codes M75 (Shoulder lesions), S42 (Fracture of shoulder and upper arm), S43 (Dislocation and sprain of joints and ligaments of shoulder girdle), and S46 (Injury of muscle, fascia and tendon at shoulder and upper arm level)
- Patients who need rehabilitation after undergoing surgical procedure and are able to perform stretch, active assistive range of motion (AAROM) or supervised active range of motion (AROM)
- between 20-80 years old
- Are able to follow motor commands
Exclusion Criteria:
- Patients with central and peripheral nervous system disease, such as cerebrovascular accident (CVA), Parkinson's disease (PD), myasthenia gravis (MG), poliomyelitis
- Patients who had shoulder contusion, vascular injury, severe crush injury and amputation
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
shoulder musculoskeletal group
The International Classification of Diseases, 10th revision (ICD-10) codes were selected before the study started and included the ICD-10 codes M75 (Shoulder lesions), S42 (Fracture of shoulder and upper arm), S43 (Dislocation and sprain of joints and ligaments of shoulder girdle), and S46 (Injury of muscle, fascia and tendon at shoulder and upper arm level)
|
usual care(rehabilitation program)
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Accuracy
Time Frame: Change from Baseline at 2 months
|
To explore the possibilities of using supervised machine learning approach to predict rehabilitation programs for shoulder musculoskeletal disorders
|
Change from Baseline at 2 months
|
|
Precision
Time Frame: Change from Baseline at 2 months
|
To explore the possibilities of using supervised machine learning approach to predict rehabilitation programs for shoulder musculoskeletal disorders
|
Change from Baseline at 2 months
|
|
Recall
Time Frame: Change from Baseline at 2 months
|
To explore the possibilities of using supervised machine learning approach to predict rehabilitation programs for shoulder musculoskeletal disorders
|
Change from Baseline at 2 months
|
|
F-1 score
Time Frame: Change from Baseline at 2 months
|
To explore the possibilities of using supervised machine learning approach to predict rehabilitation programs for shoulder musculoskeletal disorders
|
Change from Baseline at 2 months
|
|
AUC
Time Frame: Change from Baseline at 2 months
|
To explore the possibilities of using supervised machine learning approach to predict rehabilitation programs for shoulder musculoskeletal disorders
|
Change from Baseline at 2 months
|
Collaborators and Investigators
Sponsor
Sponsor
Publications and helpful links
General Publications
- Burns DM, Leung N, Hardisty M, Whyne CM, Henry P, McLachlin S. Shoulder physiotherapy exercise recognition: machine learning the inertial signals from a smartwatch. Physiol Meas. 2018 Jul 23;39(7):075007. doi: 10.1088/1361-6579/aacfd9.
- Challoumas D, Biddle M, McLean M, Millar NL. Comparison of Treatments for Frozen Shoulder: A Systematic Review and Meta-analysis. JAMA Netw Open. 2020 Dec 1;3(12):e2029581. doi: 10.1001/jamanetworkopen.2020.29581.
- Linsell L, Dawson J, Zondervan K, Rose P, Randall T, Fitzpatrick R, Carr A. Prevalence and incidence of adults consulting for shoulder conditions in UK primary care; patterns of diagnosis and referral. Rheumatology (Oxford). 2006 Feb;45(2):215-21. doi: 10.1093/rheumatology/kei139. Epub 2005 Nov 1.
- Oude Nijeweme-d'Hollosy W, van Velsen L, Poel M, Groothuis-Oudshoorn CGM, Soer R, Hermens H. Evaluation of three machine learning models for self-referral decision support on low back pain in primary care. Int J Med Inform. 2018 Feb;110:31-41. doi: 10.1016/j.ijmedinf.2017.11.010. Epub 2017 Nov 23.
- Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021 Aug;25(3):1315-1360. doi: 10.1007/s11030-021-10217-3. Epub 2021 Apr 12.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- N202206013
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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