Muscle MRI Outlining of Neuromuscular Diseases Using Artificial Intelligence
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: No intervention.
- Who it may be relevant to
- Registry conditions: Becker Muscular Dystrophy, FSHD - Facioscapulohumeral Muscular Dystrophy, Hypokalemic Periodic Paralysis. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
Background and aim: Neuromuscular diseases encompass a range of conditions affecting muscle cells, nerves, or the interaction between the two. A common pathological feature of these conditions is the pro-gressive replacement of muscle tissue with fat, which can be visualised using magnetic reso-nance imaging (MRI). MRI-based fat quantification serves as a key biomarker for disease characterisation, progression tracking, and treatment assessment. Currently, manual segmenta-tion of MRI scans for fat quantification is very time-consuming, requiring individual muscle delineation. Therefore, an artificial intelligence (AI) model is being developed to automate the segmentation. The aim of this study is to validate this AI model and assess its possibilities and limitations. Method: The study is ongoing. Retrospective MRI scans of patients with four different muscle diseases (anoctaminopathy, Becker muscular dystrophy, facioscapulohumeral muscular dystrophy, and hypokalemic periodic paralysis) are collected and manual delineation used for training the AI-model is being performed. The intramuscular fat fraction of individual muscles of the pelvis, thigh, and calf will be analysed using the AI model. The performance of the AI model will be compared to manual segmentation. The AI will be evaluated on metrics such as segmentation accuracy and time efficiency.
Interventions
- Other No intervention
No intervention.
Primary outcome measures
- Difference in fat fraction between manual and AI outlining. [Time frame: Analysis of the muscle fat fraction takes 1 hour per patient.]
Secondary outcome measures (1)
- Correlation between Manual/AI outlining discrepancies and disease severity [Time frame: The analysis of the MRI takes around an hour]
Eligibility criteria
Inclusion criteria
- Genetically verified diagnosis of neuromuscular diseases.
- Age above 18 years
Exclusion criteria
- Contraindications to perform an MRI
- Competing disorders and other muscle disorders, which may alter measurements. The investigator will decide whether the competing disorder can significantly influence the results
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Other
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT06917430 · 115991