Clinical trial NCT05858892: Comparison of an Artificial Intelligence-Assisted Rehabilitation Program for Shoulder Musculoskeletal Disorders and the Clinical Decision Making of Therapists
Official title: Comparison of an Artificial Intelligence-Assisted Rehabilitation Program for Shoulder Musculoskeletal Disorders and the Clinical Decision Making of Therapists
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Shoulder Musculoskeletal Disorders
Rehabilitation Programs
Machine Learning
Lead sponsor: Taipei Medical University Shuang Ho Hospital
Sponsor class: OTHER
First posted: 2023-05-15
Enrollment: 80
Sex: ALL
Minimum age (years): 20
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: OTHER
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.

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.


Eligibility criteria: 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

Primary outcomes:
measure: Accuracy
timeFrame: Change from Baseline at 2 months
measure: Precision
timeFrame: Change from Baseline at 2 months
measure: Recall
timeFrame: Change from Baseline at 2 months
measure: F-1 score
timeFrame: Change from Baseline at 2 months
measure: AUC
timeFrame: Change from Baseline at 2 months
Secondary outcomes:
Other outcomes:
Locations:
city: New Taipei City
country: Taiwan
facility: Shuang Ho Hospital
Study officials:
References:
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