Becker Muscular Dystrophy, FSHD - Facioscapulohumeral Muscular Dystrophy, Hypokalemic Periodic Paralysis
Conditions
Keywords
AI, muscle delineation, automated segmentation, Becker muscular dystrophy, FSHD, HypoPP
Brief summary
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
OTHERNo intervention
No intervention.
Sponsors
Rigshospitalet, Denmark
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Difference in fat fraction between manual and AI outlining. | Analysis of the muscle fat fraction takes 1 hour per patient. | The mean difference in MRI assessed intramuscular fat fraction in the lower back, thigh, and calf muscles between manual outlining and the outlining by the AI model. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Correlation between Manual/AI outlining discrepancies and disease severity | The analysis of the MRI takes around an hour | Investigate if the difference between manual outlining and AI outlining increases the more advanced stage the disease is. A correlation analysis will be made between manual/AI differences and fat fraction in lower back, thigh, and calf. |
Contacts
Primary ContactBjørk Teitsdóttir, Medical student
Backup ContactJohn Vissing, Professor
Outcome results
None listed