MRI for the diagnosis of limb girdle muscular dystrophies.
| Autor: | Bolano-Díaz C; The John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK., Verdú-Díaz J; The John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK., Díaz-Manera J; The John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.; Neuromuscular Diseases Laboratory, Insitut de Recerca de l'Hospital de la Santa Creu i Sant Pau.; Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), Barcelona, Spain. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | Current opinion in neurology [Curr Opin Neurol] 2024 Oct 01; Vol. 37 (5), pp. 536-548. Date of Electronic Publication: 2024 Aug 12. |
| DOI: | 10.1097/WCO.0000000000001305 |
| Abstrakt: | Purpose of Review: In the last 30 years, there have many publications describing the pattern of muscle involvement of different neuromuscular diseases leading to an increase in the information available for diagnosis. A high degree of expertise is needed to remember all the patterns described. Some attempts to use artificial intelligence or analysing muscle MRIs have been developed. We review the main patterns of involvement in limb girdle muscular dystrophies (LGMDs) and summarize the strategies for using artificial intelligence tools in this field. Recent Findings: The most frequent LGMDs have a widely described pattern of muscle involvement; however, for those rarer diseases, there is still not too much information available. patients. Most of the articles still include only pelvic and lower limbs muscles, which provide an incomplete picture of the diseases. AI tools have efficiently demonstrated to predict diagnosis of a limited number of disease with high accuracy. Summary: Muscle MRI continues being a useful tool supporting the diagnosis of patients with LGMD and other neuromuscular diseases. However, the huge variety of patterns described makes their use in clinics a complicated task. Artificial intelligence tools are helping in that regard and there are already some accessible machine learning algorithms that can be used by the global medical community. (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.) |
| Databáze: | MEDLINE |
| Externí odkaz: |
|