Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT05026346 |
| Other study ID # |
0002017 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
April 1, 2020 |
| Est. completion date |
September 30, 2024 |
Study information
| Verified date |
March 2024 |
| Source |
Istituto Ortopedico Rizzoli |
| Contact |
Maria Grazia Benedetti, MD |
| Phone |
+390516366236 |
| Email |
benedetti[@]ior.it |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical 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 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.
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.
