An observational study in Shoulder Musculoskeletal Disorders, Rehabilitation Programs and Machine Learning, sponsored by Taipei Medical University Shuang Ho Hospital. Status unknown at 1 site in Taiwan. Open to participants aged 20 Years to 80 Years. Per ClinicalTrials.gov, last updated 2023-05-15.
Sponsored by Taipei Medical University Shuang Ho Hospital · Observational
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.
657 studies on the registry are indexed under Musculoskeletal Diseases; 160 are open to participants now.
This study's planned enrollment of 80 is below the median of 126 across 201 observational studies indexed under Musculoskeletal Diseases.
Browse Musculoskeletal Diseases studies →Taipei Medical University Shuang Ho Hospital is the lead sponsor of 100 studies on the registry; 18 are open to participants now.
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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.
Exclusion 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)
Other: usual care
usual care(rehabilitation program)
Accuracy
To explore the possibilities of using supervised machine learning approach to predict rehabilitation programs for shoulder musculoskeletal disorders
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: Change from Baseline at 2 months
Plan to share: No
This study is status unknown, as verified in Jun 2022. You cannot join it, but the record below documents what was studied.
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Taipei Medical University Shuang Ho Hospital