Rotator Cuff TearsRehab-SPIANCT05026346
Construction of an AI System for the Automatic Supervision of Shoulder's Rehabilitation Exercises (Rehab-SPIA)
An observational study of Rotator Cuff Tears, run by Istituto Ortopedico Rizzoli at 1 site in Italy.
Trial summary
The current historical phase and the growing need for rehabilitation in the world make tele-rehabilitation systems, and e-Health in general, fundamental tools for increasing patient engagement and compliance with care, crucial elements for the preservation of the NHS from a perspective expenditure review and resource… Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Rotator Cuff Tears
- What is being tested?
- Pathologic Exercise
- How many people
- 100 participants
- Where
- 1 siteItaly
- Study dates
- 1 Apr 2020to 30 Jan 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients after arthroscopic reconstruction of rotator cuff
Who may be able to join
- Healthy subjects group:
- Adult patients> 18 years old
- Patients with no known shoulder pathologies
- Group of subjects with shoulder pathology operated on
- Adult patients> 18 years
- Suffering from orthopedic pathologies affecting the shoulder such as: outcomes of ultrasound-guided percutaneous treatment for tendon calcification, outcomes of ultrasound-guided detachment in adhesive bursitis, outcomes of proximal humerus fractures, repair of the rotator cuff, interventions for scapulo-humeral instability.
Who cannot join
- Patients with a history of opioid drug dependence or a history of substance abuse
- Patients suffering from orthopedic pathologies affecting the upper limbs in the presence of clear detectable surgical complications
- Patients with cognitive disorders (MMSE Mini Mental State Examinantion greater than or equal to 24/30).
- Patients suffering from major anamnestic or current neurological or psychiatric pathologies, severe cardiopulmonary, hepatic or renal pathologies that contraindicate participation in the study.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Construction of an Artificial Intelligence System for the Remote Automatic Supervision of Shoulder's Rehabilitation Exercises
The current historical phase and the growing need for rehabilitation in the world make tele-rehabilitation systems, and e-Health in general, fundamental tools for increasing patient engagement and compliance with care, crucial elements for the preservation of the NHS from a perspective expenditure review and resource optimization. In particular, the rehabilitation patient has on average an adherence to the Home Exercise Program (HEP) between 30-50%, to which is frequently added a reduced effectiveness of motor learning due to the lack of feedback on the accuracy of the gesture, as is the case. it happens in the hospital or outpatient setting under the supervision of a therapist.
The new computational approaches for the analysis of data on human movement, aimed at the development of algorithms to automatically supervise the accuracy of the patient's gesture during home self-treatment exercise such as those based on Artificial Intelligence (AI) and Machine Learning (ML), especially those of the latest generation, called sub-symbolics (or connectionists) can help.
Read more
Among the most promising approaches are. Given the importance of the Home Exercise Program in shoulder disease, it was decided to select a population of patients affected by the main pathologies affecting this joint.
The main objective of the study is to create and validate a software tool for the automatic and expert analysis of the correct execution of the main rehabilitation exercises for the functional recovery of the shoulder following orthopedic pathologies.
Read the detailed description
The current historical phase and the growing need for rehabilitation in the world make tele-rehabilitation systems, and e-Health in general, fundamental tools for increasing patient engagement and compliance with care, crucial elements for the preservation of the NHS from a perspective expenditure review and resource optimization .
In particular, the rehabilitation patient has on average an adherence to the Home Exercise Program (HEP) between 30-50%, to which is frequently added a reduced effectiveness of motor learning due to the lack of feedback on the accuracy of the gesture, as it happens in the hospital or outpatient setting under the supervision of a therapist.
The new computational approaches for the analysis of data on human movement, aimed at the development of algorithms to automatically supervise the accuracy of the patient's gesture during the exercise of home self-treatment, attempt to solve this last critical issue.
Among the most promising approaches are those based on Artificial Intelligence (AI) and Machine Learning (ML), in particular those of the latest generation, called sub-symbolic (or connectionist).
These algorithms arouse a lot of interest for their ability to automatically extract the salient properties of the movement, reducing the intervention of experts to the collection of all the data, and to the possible labeling of the examples (5) In any case, the literature shows a lack of models developed with the direct involvement of clinicians and a scarcity of data sets created with patient populations.
Furthermore, most of the models present in the literature have been created using numerous input devices, often with a high technological rate with considerable costs for implementing a possible service at the patient's home.
For these reasons we want to create a specialist clinical dataset, starting only from the videos of the exercises, involving specific populations by pathology and built on the basis of clinical judgment. With these characteristics, this project aims to automate the motion analysis process as much as possible, enormously reducing the costs deriving from the use of technologies and minimizing human error, all by exploiting the most recent computational approaches in order to create a useful and low-cost tool for home functional re-education.
Given the importance of the Home Exercise Program in shoulder disease, it was decided to select a population of patients affected by the main pathologies affecting this joint.
The main objective of the study is to create and validate a software tool for the automatic and expert analysis of the correct execution of the main rehabilitation exercises for the functional recovery of the shoulder following orthopedic pathologies.
- Conditions
- Rotator Cuff Tears
- Being tested
- Pathologic Exercise
- Medical subject headings
- Rotator Cuff Injuries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Pathologic ExerciseDiagnostic test
- In the first phase of the project, a series of 5 active shoulder mobilization exercises characterized by an adequate range of motion will be tested to verify the set up for the video recording.
- In the second phase shoulder movements will be recorder by a smartphone.
- A questionnaire will be used and adapted on the basis of which to evaluate the correctness of the exercises performed by each healthy subject / patient. This questionnaire will provide a Clinical Score (CS) which assigns a numerical value to the patient's overall performance for each repetition.
- The videos of each repetition of exercises performed by the healthy subjects / patients will then be evaluated by two different clinicians, blinded, using the questionnaire.
- The Artificial Intelligence learning algorithm will be able to output an evaluation score that will be compared with that produced by clinicians.
| Study group | Role | What they receive |
|---|---|---|
| Healthy | Healthy subjects, wothout shoulder pathology Receives: Diagnostic Test: Pathologic Exercise | |
| Rotator cuff tears | Patients after arthroscopic reconstruction of rotator cuff Receives: Diagnostic Test: Pathologic Exercise |
What is being measured
The primary outcome is the main question the study is designed to answer.
Correctness of the shoulder movement
A questionnaire in which the clinician will describe the correctenss of the shoulder movement will be used and compared with the attribution by the Artificial Intelligence software
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Aug 2021
- Study start1 Apr 2020
- Primary completion30 Sep 2024
- Study completion30 Jan 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Rotator Cuff TearsStudies of the same condition, open and under way.
- More trials run by Istituto Ortopedico RizzoliThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 100 participants (actual)
Sponsor and investigators
- Lead sponsor
- Istituto Ortopedico Rizzoli
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Maria Grazia Benedetti, MD, Principal investigator, Istituto Ortopedico Rizzoli
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05026346
- Sponsor's protocol ID
- 0002017
- Acronym
- Rehab-SPIA
- Status last verified
- Mar 2025
- First submitted
- 27 Aug 2021
- Last updated
- 19 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Construction of an Artificial Intelligence System for the Remote Automatic Supervision of Shoulder's Rehabilitation Exercises
- ClinicalTrials.gov ID
- NCT05026346
- Acronym / sponsor ID
- Rehab-SPIA / 0002017
- Conditions
- Rotator Cuff Tears
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 100 participants (actual)
- Lead sponsor
- Istituto Ortopedico RizzoliUniversity, hospital or other
- Study start
- 1 Apr 2020 (actual)
- Primary completion
- 30 Sep 2024 (actual)
- Study completion
- 30 Jan 2025 (actual)
- First posted
- 30 Aug 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The current historical phase and the growing need for rehabilitation in the world make tele-rehabilitation systems, and e-Health in general, fundamental tools for increasing patient engagement and compliance with care, crucial elements for the preservation of the NHS from a perspective expenditure review and resource optimization. In particular, the rehabilitation patient has on average an adherence to the Home Exercise Program (HEP) between 30-50%, to which is frequently added a reduced effectiveness of motor learning due to the lack of feedback on the accuracy of the gesture, as is the case. it happens in the hospital or outpatient setting under the supervision of a therapist.
The new computational approaches for the analysis of data on human movement, aimed at the development of algorithms to automatically supervise the accuracy of the patient's gesture during home self-treatment exercise such as those based on Artificial Intelligence (AI) and Machine Learning (ML), especially those of the latest generation, called sub-symbolics (or connectionists) can help.
Among the most promising approaches are. Given the importance of the Home Exercise Program in shoulder disease, it was decided to select a population of patients affected by the main pathologies affecting this joint.
The main objective of the study is to create and validate a software tool for the automatic and expert analysis of the correct execution of the main rehabilitation exercises for the functional recovery of the shoulder following orthopedic pathologies.
Detailed description
The current historical phase and the growing need for rehabilitation in the world make tele-rehabilitation systems, and e-Health in general, fundamental tools for increasing patient engagement and compliance with care, crucial elements for the preservation of the NHS from a perspective expenditure review and resource optimization .
In particular, the rehabilitation patient has on average an adherence to the Home Exercise Program (HEP) between 30-50%, to which is frequently added a reduced effectiveness of motor learning due to the lack of feedback on the accuracy of the gesture, as it happens in the hospital or outpatient setting under the supervision of a therapist.
The new computational approaches for the analysis of data on human movement, aimed at the development of algorithms to automatically supervise the accuracy of the patient's gesture during the exercise of home self-treatment, attempt to solve this last critical issue.
Among the most promising approaches are those based on Artificial Intelligence (AI) and Machine Learning (ML), in particular those of the latest generation, called sub-symbolic (or connectionist).
These algorithms arouse a lot of interest for their ability to automatically extract the salient properties of the movement, reducing the intervention of experts to the collection of all the data, and to the possible labeling of the examples (5) In any case, the literature shows a lack of models developed with the direct involvement of clinicians and a scarcity of data sets created with patient populations.
Furthermore, most of the models present in the literature have been created using numerous input devices, often with a high technological rate with considerable costs for implementing a possible service at the patient's home.
For these reasons we want to create a specialist clinical dataset, starting only from the videos of the exercises, involving specific populations by pathology and built on the basis of clinical judgment. With these characteristics, this project aims to automate the motion analysis process as much as possible, enormously reducing the costs deriving from the use of technologies and minimizing human error, all by exploiting the most recent computational approaches in order to create a useful and low-cost tool for home functional re-education.
Given the importance of the Home Exercise Program in shoulder disease, it was decided to select a population of patients affected by the main pathologies affecting this joint.
The main objective of the study is to create and validate a software tool for the automatic and expert analysis of the correct execution of the main rehabilitation exercises for the functional recovery of the shoulder following orthopedic pathologies.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Healthy | – | Diagnostic Test: Pathologic ExerciseHealthy subjects, wothout shoulder pathology |
| Rotator cuff tears | – | Diagnostic Test: Pathologic ExercisePatients after arthroscopic reconstruction of rotator cuff |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients after arthroscopic reconstruction of rotator cuff
Key inclusion criteria (6 listed)
- Healthy subjects group:
- Group of subjects with shoulder pathology operated on
Key exclusion criteria (4 listed)
- Patients with a history of opioid drug dependence or a history of substance abuse
- Patients suffering from orthopedic pathologies affecting the upper limbs in the presence of clear detectable surgical complications
- Patients with cognitive disorders (MMSE Mini Mental State Examinantion greater than or equal to 24/30).
- Patients suffering from major anamnestic or current neurological or psychiatric pathologies, severe cardiopulmonary, hepatic or renal pathologies that contraindicate participation in the study.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Pathologic Exercise1. In the first phase of the project, a series of 5 active shoulder mobilization exercises characterized by an adequate range of motion will be tested to verify the set up for the video recording. 2. In the second phase shoulder movements will be recorder by a smartphone. 3. A questionnaire will be used and adapted on the basis of which to evaluate the correctness of the exercises performed by each healthy subject / patient. This questionnaire will provide a Clinical Score (CS) which assigns a numerical value to the patient's overall performance for each repetition. 4. The videos of each repetition of exercises performed by the healthy subjects / patients will then be evaluated by two different clinicians, blinded, using the questionnaire. 5. The Artificial Intelligence learning algorithm will be able to output an evaluation score that will be compared with that produced by clinicians. | Diagnostic test | Healthy, Rotator cuff tears |
Primary outcome
Correctness of the shoulder movement
Time frame: 12 months
A questionnaire in which the clinician will describe the correctenss of the shoulder movement will be used and compared with the attribution by the Artificial Intelligence software
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Mar 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Maria Grazia Benedetti, MD principal investigator
Istituto Ortopedico Rizzoli
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Rotator Cuff Tears, run by Istituto Ortopedico Rizzoli at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 19 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Correctness of the shoulder movement (time frame: 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Mar 2025.