ArticleEuropean radiology2023
Assessment of idiopathic inflammatory myopathy using a deep learning method for muscle T2 mapping segmentation.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating Machine Learning into Myositis Research: a Systematic Review.Clinical reviews in allergy & immunology · 2025Pooled it
- Prediction of late-pregnancy pelvic floor dysfunction: a nomogram model based on proton density fat fraction and synthetic magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- AI-powered CT imaging for early detection of subclinical pulmonary fibrosis in antisynthetase syndrome: a new era in autoimmune lung diagnostics.Annals of medicine and surgery (2012) · 2025Article
- A deep learning algorithm for automatic 3D segmentation and quantification of hamstrings musculotendon injury from MRI.Scientific reports · 2025Article
- A Minimal Annotation Pipeline for Deep Learning Segmentation of Skeletal Muscles.NMR in biomedicine · 2025Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- MGB-Unet: An Improved Multiscale Unet with Bottleneck Transformer for Myositis Segmentation from Ultrasound Images.Journal of imaging informatics in medicine · 2025Article
- The utility of muscle magnetic resonance imaging in idiopathic inflammatory myopathies: a scoping review.Frontiers in immunology · 2025Article
- Quantitative magnetic resonance imaging of paraspinal muscles for assessing chronic non-specific low back pain in young adults: a prospective case-control study.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024Article
- Deep learning in rheumatological image interpretation.Nature reviews. Rheumatology · 2024Review
- Automatic segmentation of lower limb muscles from MR images of post-menopausal women based on deep learning and data augmentation.PloS one · 2024Article
- Muscle diffusion MRI reveals autophagic buildup in a mouse model for Pompe disease.Scientific reports · 2023Article
- Review
- Correlation analysis of quantitative MRI measurements of thigh muscles with histopathology in patients with idiopathic inflammatory myopathy.European radiology experimental · 2023Article
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13 authors.
Funding
Abstract
objectiveTo investigate the utility of an automatic deep learning (DL) method for segmentation of T2 maps in patients with idiopathic inflammatory myopathy (IIM) against healthy controls, and also the association of quantitative T2 values in patients with laboratory and pulmonary findings.
methodsStructural MRI and T2 mapping of bilateral thigh muscles from patients with IIM and healthy volunteers were segmented using dedicated software based on a pre-trained convolutional neural network. Incremental and federated learning were implemented for continuous adaptation and improvement. Muscle T2 values derived from DL segmentation were compared between patients and healthy controls, and T2 values of patients were further analyzed with serum muscle enzymes, and interstitial lung disease (ILD) which was diagnosed and graded based on chest HRCT.
resultsOverall, 64 patients (27 patients with dermatomyositis, 29 with polymyositis, and 8 with antisynthetase syndrome (ASS)) and 10 healthy controls were included. By using DL-based muscle segmentation, T2 values generated from T2 maps accurately differentiated patients from those of controls (p < 0.001) with a cutoff value of 36.4 ms (sensitivity 96.9%, and specificity 100%). In patients with IIM, muscle T2 values positively correlated with all the serum muscle enzymes (all p < 0.05). ILD score of patients with ASS was markedly higher than that of those without ASS (p = 0.011), while dissociation between the severity of muscular involvement and ILD was observed (p = 0.080).
conclusionAutomatic DL could be used to segment thigh muscles and help quantitatively assess muscular inflammation of IIM through T2 mapping. KEY POINTS: • Muscle T2 mapping automatically segmented by deep learning can differentiate IIM from healthy controls. • T2 value, an indicator of active muscle inflammation, positively correlates with serum muscle enzymes. • T2 mapping can detect muscle disease in patients with normal muscle enzyme levels.
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