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Muscular dystrophies (MD) are a group of genetic disorders caused by mutations in genes involved in muscular structure and function. They are characterized by muscular weakness and dystrophic histopathological changes. MRI is a supportive diagnostic tool due to its sensitivity in detecting muscle fat infiltration patterns (FIP). Different FIPs have been associated with specific MDs; for this reason, MRI has gained a role in their diagnosis. Heatmaps have been used to represent MRI's FIP. However, it is difficult to compare the FIP of patients with different diseases using heatmaps because of their high dimensionality. For example, a lower limb MRI heatmap can have up to 70 muscles. We hypothesized that dimensionality reduction techniques (DRT) could effectively represent MRI FIP in a low-dimensional space, allowing easier visualization and comparison of patients. Four DRTs were compared: PCA, ISOMAP, t-SNE, and UMAP. An open MRI's FIP database of 975 patients with a genetically confirmed diagnosis of 10 different MDs was used. The database consists of lower limb MRI muscle fat infiltration scores semi-quantitatively graded through the Mercuri scale from T1w images. To quantify the performance of the DRTs, a K-means clustering algorithm was run 20 times with different seeds over the dimensionality-reduced coordinates. Six metrics of clustering results performance were compared (Misclassification fraction, Homogeneity, Completeness, V-measure, Adjusted Rand Index, Adjusted mutual information, and Silhouette coefficient). These results were compared using unpaired t-tests and corrected for multiple comparisons. UMAP significantly outperformed the other techniques, with a small but significant difference with t-SNE in all metrics except for homogeneity. t-SNE outperformed PCA and ISOMAP in all metrics, and similarly, ISOMAP outperformed PCA. In this way, we showed that DRTs are suitable tools that can facilitate the visualization of the MRI's FIP of patients with MDs. Although further research is needed to validate these results in diverse patient populations and clinical settings, DRTs, especially t-SNE and uMAP, are promising tools that may aid in the visualization of fat infiltration patterns in patients with DMs, which may facilitate the diagnosis of these disorders in the clinical setting. [ABSTRACT FROM AUTHOR]

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.)

We investigated the comorbidities, associated factors, and the relationship between anthropometric measures and respiratory function and functional abilities in adults with Duchenne muscular dystrophy (DMD). This was a single-centre cross-sectional study in genetically diagnosed adults with DMD (>16 years old). Univariate and multivariate analyses identified factors associated with dysphagia, constipation, Body Mass Index (BMI), and weight. Regression analysis explored associations between BMI, weight, and respiratory/motor abilities. We included 112 individuals (23.4 ± 5.2 years old), glucocorticoid-treated 66.1 %. The comorbidities frequency was 61.6 % scoliosis (61.0 % of them had spinal surgery), 36.6 % dysphagia, 36.6 % constipation, and 27.8 % urinary conditions. The use of glucocorticoids delayed the time to spinal surgery. The univariate analysis revealed associations between dysphagia and constipation with age, lack of glucocorticoid treatment, and lower respiratory and motor function. In the multivariate analysis, impaired cough ability remained as the factor consistently linked to both conditions. Constipation associated with lower BMI and weight. BMI and weight positively correlated with respiratory parameters, but they did not associate with functional abilities. Glucocorticoids reduce the frequency of comorbidities in adults with DMD. The ability to cough can help identifying dysphagia and constipation. Lower BMI and weight in individuals with DMD with compromised respiratory function may suggest a higher calories requirement., Competing Interests: Declaration of competing interest The rest of the authors report no competing interests., (Copyright © 2024. Published by Elsevier B.V.)

Becker muscular dystrophy (BMD) is characterised by fiber loss and expansion of fibrotic and adipose tissue. Several cells interact locally in what is known as the degenerative niche. We analysed muscle biopsies of controls and BMD patients at early, moderate and advanced stages of progression using Hyperion imaging mass cytometry (IMC) by labelling single sections with 17 markers identifying different components of the muscle. We developed a software for analysing IMC images and studied changes in the muscle composition and spatial correlations between markers across disease progression. We found a strong correlation between collagen-I and the area of stroma, collagen-VI, adipose tissue, and M2-macrophages number. There was a negative correlation between the area of collagen-I and the number of satellite cells (SCs), fibres and blood vessels. The comparison between fibrotic and non-fibrotic areas allowed to study the disease process in detail. We found structural differences among non-fibrotic areas from control and patients, being these latter characterized by increase in CTGF and in M2-macrophages and decrease in fibers and blood vessels. IMC enables to study of changes in tissue structure along disease progression, spatio-temporal correlations and opening the door to better understand new potential pathogenic pathways in human samples., (© 2024. The Author(s).)

The identification of disease-characteristic patterns of muscle fatty replacement in magnetic resonance imaging (MRI) is helpful for diagnosing neuromuscular diseases. In the Clinical Outcome Study of Dysferlinopathy, eight diagnostic rules were described based on MRI findings. Our aim is to confirm that they are useful to differentiate dysferlinopathy (DYSF) from other genetic muscle diseases (GMD). The rules were applied to 182 MRIs of dysferlinopathy patients and 1000 MRIs of patients with 10 other GMD. We calculated sensitivity (S), specificity (Sp), positive and negative predictive values (PPV/NPV) and accuracy (Ac) for each rule. Five of the rules were more frequently met by the DYSF group. Patterns observed in patients with FKRP, ANO5 and CAPN3 myopathies were similar to the DYSF pattern, whereas patterns observed in patients with OPMD, laminopathy and dystrophinopathy were clearly different. We built a model using the five criteria more frequently met by DYSF patients that obtained a S 95.9%, Sp 46.1%, Ac 66.8%, PPV 56% and NPV 94% to distinguish dysferlinopathies from other diseases. Our findings support the use of MRI in the diagnosis of dysferlinopathy, but also identify the need to externally validate "disease-specific" MRI-based diagnostic criteria using MRIs of other GMD patients., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Background: The diagnosis of patients with mutations in the VCP gene can be complicated due to their broad phenotypic spectrum including myopathy, motor neuron disease and peripheral neuropathy. Muscle MRI guides the diagnosis in neuromuscular diseases (NMDs); however, comprehensive muscle MRI features for VCP patients have not been reported so far., Methods: We collected muscle MRIs of 80 of the 255 patients who participated in the "VCP International Study" and reviewed the T1-weighted (T1w) and short tau inversion recovery (STIR) sequences. We identified a series of potential diagnostic MRI based characteristics useful for the diagnosis of VCP disease and validated them in 1089 MRIs from patients with other genetically confirmed NMDs., Results: Fat replacement of at least one muscle was identified in all symptomatic patients. The most common finding was the existence of patchy areas of fat replacement. Although there was a wide variability of muscles affected, we observed a common pattern characterized by the involvement of periscapular, paraspinal, gluteal and quadriceps muscles. STIR signal was enhanced in 67% of the patients, either in the muscle itself or in the surrounding fascia. We identified 10 diagnostic characteristics based on the pattern identified that allowed us to distinguish VCP disease from other neuromuscular diseases with high accuracy., Conclusions: Patients with mutations in the VCP gene had common features on muscle MRI that are helpful for diagnosis purposes, including the presence of patchy fat replacement and a prominent involvement of the periscapular, paraspinal, abdominal and thigh muscles., (© 2023. The Author(s).)

Gaucher disease (GD) is a genetic lysosomal disorder characterized by high bone marrow (BM) involvement and skeletal complications. The pathophysiology of these complications is not fully elucidated. Magnetic resonance imaging (MRI) is the gold standard to evaluate BM. This study aimed to apply machine-learning techniques in a cohort of Spanish GD patients by a structured bone marrow MRI reporting model at diagnosis and follow-up to predict the evolution of the bone disease. In total, 441 digitalized MRI studies from 131 patients (M: 69, F:62) were reevaluated by a blinded expert radiologist who applied a structured report template. The studies were classified into categories carried out at different stages as follows: A: baseline; B: between 1 and 4 y of follow-up; C: between 5 and 9 y; and D: after 10 years of follow-up. Demographics, genetics, biomarkers, clinical data, and cumulative years of therapy were included in the model. At the baseline study, the mean age was 37.3 years (1-80), and the median Spanish MRI score (S-MRI) was 8.40 (male patients: 9.10 vs. female patients: 7.71) ( p < 0.001). BM clearance was faster and deeper in women during follow-up. Genotypes that do not include the c.1226A>G variant have a higher degree of infiltration and complications ( p = 0.017). A random forest machine-learning model identified that BM infiltration degree, age at the start of therapy, and femur infiltration were the most important factors to predict the risk and severity of the bone disease. In conclusion, a structured bone marrow MRI reporting in GD is useful to standardize the collected data and facilitate clinical management and academic collaboration. Artificial intelligence methods applied to these studies can help to predict bone disease complications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Valero-Tena, Roca-Espiau, Verdú-Díaz, Diaz-Manera, Andrade-Campos and Giraldo.)

Objective: Genetic diagnosis of muscular dystrophies (MDs) has classically been guided by clinical presentation, muscle biopsy, and muscle MRI data. Muscle MRI suggests diagnosis based on the pattern of muscle fatty replacement. However, patterns overlap between different disorders and knowledge about disease-specific patterns is limited. Our aim was to develop a software-based tool that can recognize muscle MRI patterns and thus aid diagnosis of MDs., Methods: We collected 976 pelvic and lower limbs T1-weighted muscle MRIs from 10 different MDs. Fatty replacement was quantified using Mercuri score and files containing the numeric data were generated. Random forest supervised machine learning was applied to develop a model useful to identify the correct diagnosis. Two thousand different models were generated and the one with highest accuracy was selected. A new set of 20 MRIs was used to test the accuracy of the model, and the results were compared with diagnoses proposed by 4 specialists in the field., Results: A total of 976 lower limbs MRIs from 10 different MDs were used. The best model obtained had 95.7% accuracy, with 92.1% sensitivity and 99.4% specificity. When compared with experts on the field, the diagnostic accuracy of the model generated was significantly higher in a new set of 20 MRIs., Conclusion: Machine learning can help doctors in the diagnosis of muscle dystrophies by analyzing patterns of muscle fatty replacement in muscle MRI. This tool can be helpful in daily clinics and in the interpretation of the results of next-generation sequencing tests., Classification of Evidence: This study provides Class II evidence that a muscle MRI-based artificial intelligence tool accurately diagnoses muscular dystrophies., (© 2020 American Academy of Neurology.)