Neuromuscular disorders in the omics era

Dabaj, Ivana; Ducatez, Franklin; Marret, Stéphane; Bekri, Soumeya; Tebani, Abdellah

doi:10.1016/j.cca.2023.117691

Cited by 1 publication

(1 citation statement)

References 153 publications

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“…To date, outcome measures in MDs and congenital myopathies include neuromuscular examination with muscle strength testing, functional motor scales with timed tasks (i.e., Six Minute Walking test, North Star Ambulatory Assessment scale, Performance of the Upper Limb scale, among others), and global physical evaluation, comprehensive of functional cardiac and respiratory assessments. However, based on the upsurge of diagnostic sensitivity and capabilities and surrogate fluid biomarkers tracking disease progression (although with some limitations) [ 5 ], there is clear evidence of clinical dissimilarities, prompting further research on the natural history and deep phenotyping of patients. In the past years, muscle MRI has grown as a useful and informative tool in neuromuscular disorders (NMDs), both at a research and clinical level.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Automated Segmentation of Skeletal Muscle and Subcutaneous Adipose Tissue on Thigh MRI in Muscular Dystrophy Patients

Aringhieri,

Astrea,

Marfisi

et al. 2024

JFMK

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We aim to develop a deep learning-based algorithm for automated segmentation of thigh muscles and subcutaneous adipose tissue (SAT) from T1-weighted muscle MRIs from patients affected by muscular dystrophies (MDs). From March 2019 to February 2022, adult and pediatric patients affected by MDs were enrolled from Azienda Ospedaliera Universitaria Pisana, Pisa, Italy (Institution 1) and the IRCCS Stella Maris Foundation, Calambrone-Pisa, Italy (Institution 2), respectively. All patients underwent a bilateral thighs MRI including an axial T1 weighted in- and out-of-phase (dual-echo). Both muscles and SAT were manually and separately segmented on out-of-phase image sets by a radiologist with 6 years of experience in musculoskeletal imaging. A U-Net1 and U-Net3 were built to automatically segment the SAT, all the thigh muscles together and the three muscular compartments separately. The dataset was randomly split into the on train, validation, and test set. The segmentation performance was assessed through the Dice similarity coefficient (DSC). The final cohort included 23 patients. The estimated DSC for U-Net1 was 96.8%, 95.3%, and 95.6% on train, validation, and test set, respectively, while the estimated accuracy for U-Net3 was 94.1%, 92.9%, and 93.9%. Both of the U-Nets achieved a median DSC of 0.95 for SAT segmentation. The U-Net1 and the U-Net3 achieved an optimal agreement with manual segmentation for the automatic segmentation. The so-developed neural networks have the potential to automatically segment thigh muscles and SAT in patients affected by MDs.

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Section: Introductionmentioning

confidence: 99%