Neuromuscular diseases Musculoskeletal Diseases
Conditions
Interventions
The Newcastle University research team will score muscle MRI images already obtained for diagnosis in clinics using the Lamminen-Mercuri scale (Diaz-Manera 2015). The numerical data from these scores
Sponsors
Newcastle University
Eligibility
Sex/Gender
All
Inclusion criteria
Inclusion criteria: This is a data archive study and no patients will be recruited into the study. This study will be using historical muscle MRI scans as well as limited patient data (i.e. age, sex, and genetic diagnosis of muscle disease)
Exclusion criteria
Exclusion criteria: This is a data archive study and no patients will be recruited into the study. This study will be using historical muscle MRI scans as well as limited patient data (i.e. age, sex, and genetic diagnosis of muscle disease)
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| 1. To develop an artificial intelligence tool using machine learning that can guide the genetic diagnosis of muscle disorders based on the analysis of muscle MRIs. 2. To develop an artificial intelligence tool using a methodology called neural network, which will automatically identify and segment pelvic and leg muscles to quantify the amount of fat present in the skeletal muscles. 3. To collect many muscle MRIs of patients with different genetically confirmed muscles diseases. 4. To score fat replacement of all muscles of the pelvis and legs of the new cohort of patients included in the study. 5. To generate a new version of the MYO-Share platform containing MYO-Guide and the automatic segmentation tool. The MRI images of muscles from patients who have a neuromuscular disease will be included in this study. The purpose of this image data collection is twofold: 1) to inform the artificial intelligence tool used for diagnosis and 2) to inform the artificial intelligence tool used to automatically segment MRIs. For the diagnosis tool, the anonymised MRI images and patient data will be obtained either via an online platform (MYO-Share) uploaded by NHS sites and health care settings around the world or from data archives and Newcastle University. The automatic segmentation software will be able to identify and delineate each single muscle in the pelvis, thigh, and leg. To build the automatic segmentation algorithm, anonymised MRIs will be obtained from Newcastle University. We will use a neural network approach to identify the muscles on the MRI and quantify the amount of fat present in the muscles. On a first step, we will delineate manually all muscles of the lower limbs using an imaging delineation tool and assign a label of each muscle creating what is known as masks. On a second step, we will use all the masks generated to train a neural network that wil | — |
Secondary
| Measure | Time frame |
|---|---|
| There are no secondary outcome measures | — |
Countries
Canada, Chile, Denmark, England, France, Italy, Korea, South, Spain, United Kingdom
Outcome results
None listed