MRI for the diagnosis of limb girdle muscular dystrophies

被引:1
|
作者
Bolano-Diaz, Carla [1 ,2 ]
Verdu-Diaz, Jose [1 ,2 ]
Diaz-Manera, Jordi [1 ,2 ,3 ,4 ]
机构
[1] Newcastle Univ, John Walton Muscular Dystrophy Res Ctr, Newcastle Upon Tyne, England
[2] Newcastle Hosp NHS Fdn Trust, Newcastle Upon Tyne, England
[3] Insitut Recerca Hosp Santa Creu i St Pau, Neuromuscular Dis Lab, Barcelona, Spain
[4] Ctr Invest Biomed Red Enfermedades Raras CIBERER, Barcelona, Spain
基金
英国医学研究理事会;
关键词
artificial intelligence; limb girdle muscular dystrophy; muscle MRI; MUSCLE MRI; BETHLEM MYOPATHY; MUTATIONAL SPECTRUM; PHENOTYPIC SPECTRUM; THIGH MUSCLES; ANOCTAMIN; FOLLOW-UP; CGH ARRAY; DYSFERLINOPATHY; VARIABILITY;
D O I
10.1097/WCO.0000000000001305
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Purpose of reviewIn 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 findingsThe 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.SummaryMuscle 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.
引用
收藏
页码:536 / 548
页数:13
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