Comparison of an Artificial Intelligence-Assisted Rehabilitation Program for Shoulder Musculoskeletal Disorders and the Clinical Decision Making of Therapists

Machine Learning Clinical Trial

Official title:

Comparison of an Artificial Intelligence-Assisted Rehabilitation Program for Shoulder Musculoskeletal Disorders and the Clinical Decision Making of Therapists

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05858892
• Other study ID #: N202206013
• Status: Recruiting
• Start date: July 11, 2022
• Est. completion date: April 30, 2024

Study information

• Verified date: June 2022
• Source: Taipei Medical University Shuang Ho Hospital
• Contact: Hanyun Hsiao, master
• Phone: +88622490088
• Email: 10252[@]s.tmu.edu.tw
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

People with shoulder musculoskeletal disorders among middle-aged and older adults have the highest need of rehabilitation services. The population growth and aging society subsequently increase the number of disabled people, the healthcare costs and the needs for healthcare professionals. The evidence exists to support the beneficial effect of exercises on function and quality of life. Traditionally, a rehabilitation program is designed by therapists for each patient depending on their conditions. In recent years, AI is increasingly being employed in the field of physical and rehabilitation medicine, however, there is no study of applying AI in predicting rehabilitation programs for shoulder musculoskeletal disorders. The main purpose of this study is to explore the possibilities of using supervised machine learning approach to predict rehabilitation programs for shoulder musculoskeletal disorders. Twenty-three features are identified based on shoulder range of motion, pain, whether or not perform surgical procedure. Each exercise is considered as a label with a total of twenty-five exercises. Dataset is collected by clinical therapists to develop and train the model. Each patient has to receive at least two months of rehabilitation and two times of evaluation. Logistic regression, support vector machine and random forest are used to build the computational model. Accuracy, precision, recall, F-1 score and AUC are used to evaluate the performance of the computational model in machine learning. After training, we compare the consistency of rehabilitation programs predicted by using machine learning model and the clinical decision making of therapists.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 80
• Est. completion date: April 30, 2024
• Est. primary completion date: April 30, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 20 Years to 80 Years

Eligibility

Inclusion Criteria:

1. 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)

2. 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)

3. between 20-80 years old

4. Are able to follow motor commands

Exclusion Criteria:

1. Patients with central and peripheral nervous system disease, such as cerebrovascular accident (CVA), Parkinson's disease (PD), myasthenia gravis (MG), poliomyelitis

2. Patients who had shoulder contusion, vascular injury, severe crush injury and amputation

Related Conditions & MeSH terms

• Machine Learning
• Musculoskeletal Diseases
• Shoulder Musculoskeletal Disorders

Intervention

Other:
• usual care
usual care(rehabilitation program)

Locations

Country	Name	City	State
Taiwan	Shuang Ho Hospital	New Taipei City

Sponsors (1)

Lead Sponsor	Collaborator
Taipei Medical University Shuang Ho Hospital

Country where clinical trial is conducted

Taiwan, 

References & Publications (5)

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Accuracy	To explore the possibilities of using supervised machine learning approach to predict rehabilitation programs for shoulder musculoskeletal disorders	Change from Baseline at 2 months
Primary	Precision	To explore the possibilities of using supervised machine learning approach to predict rehabilitation programs for shoulder musculoskeletal disorders	Change from Baseline at 2 months
Primary	Recall	To explore the possibilities of using supervised machine learning approach to predict rehabilitation programs for shoulder musculoskeletal disorders	Change from Baseline at 2 months
Primary	F-1 score	To explore the possibilities of using supervised machine learning approach to predict rehabilitation programs for shoulder musculoskeletal disorders	Change from Baseline at 2 months
Primary	AUC	To explore the possibilities of using supervised machine learning approach to predict rehabilitation programs for shoulder musculoskeletal disorders	Change from Baseline at 2 months