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

NCT05858892 | Unknown

• 1 other identifier: N202206013
• Study Type: observational
• Target: 80
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Shoulder Musculoskeletal Disorders; Rehabilitation Programs; Machine Learning

Outcome Measures

Primary Outcomes (5)

• 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

• 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

• 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

• 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

• 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

Study Arms (1)

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)

Other: usual care

Interventions

usual care OTHER

usual care(rehabilitation program)

shoulder musculoskeletal group

Eligibility Criteria

• Age: 20 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Musculoskeletal disorders that commonly cause shoulder pain in the clinic include Adhesive Capsulitis of shoulder (AC or frozen shoulder), Rotator Cuff Tear or Rupture (RCT), and Shoulder Impingement Syndrome (SIS). The incidence of AC in the general population is approximately 2-5%, most commonly occurring in women aged 40-60 years; the incidence of RCT is 20.7% and increases with age, most commonly associated with SIS is the most frequent cause of shoulder pain, accounting for about 44-65% of cases, usually affecting people over the age of 40.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Taipei Medical University Shuang Ho Hospital: lead

Study Sites (1)

Shuang Ho Hospital

New Taipei City, 235, Taiwan

RECRUITING

Related 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.

  PMID: 29952759 BACKGROUND

• 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.

  PMID: 33326025 BACKGROUND

• 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.

  PMID: 16263781 BACKGROUND

• 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.

  PMID: 29331253 BACKGROUND

• 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.

  PMID: 33844136 BACKGROUND

Central Study Contacts

Hanyun Hsiao, master

CONTACT

+88622490088; 10252@s.tmu.edu.tw

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 5, 2023
• First Posted: May 15, 2023
• Study Start: July 11, 2022
• Primary Completion: April 30, 2024
• Study Completion: April 30, 2024
• Last Updated: May 15, 2023

Record last verified: 2022-06

Data Sharing

• IPD Sharing: Will not share

Locations

TW (1)