ArticleBMC neurology2021
A deep learning model for diagnosing dystrophinopathies on thigh muscle MRI images.
Article in BMC neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 19 citations in OpenAlex.
- Artificial Intelligence in Neuromuscular Diseases: Opportunities for a Data-Scarce Field.Neurology and therapy · 2026Review
- Emerging therapeutic strategies in muscular dystrophy: an updated review on pathogenesis and treatment advances.Molecular biology reports · 2026Review
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- Training Together, Diagnosing Better: Federated Learning for Collagen VI-Related Dystrophies.ArXiv · 2025Article
- Automated Classification of Neuromuscular Diseases Using Thigh Muscle MRI With Model Interpretations.Journal of cachexia, sarcopenia and muscle · 2025Article
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- Machine learning-based radiomics using MRI to differentiate early-stage Duchenne and Becker muscular dystrophy in children.BMC musculoskeletal disorders · 2025Article
- Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model.Bioengineering (Basel, Switzerland) · 2024Article
- Artificial Intelligence Applications in the Diagnosis of Neuromuscular Diseases: A Narrative Review.Cureus · 2023Review
- SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for Prior-Informed Assessment of Muscle Function and Pathology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Roles of Skeletal Muscle in Development: A Bioinformatics and Systems Biology Overview.Advances in anatomy, embryology, and cell biology · 2023Article
- Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning.Frontiers in bioengineering and biotechnology · 2021Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDystrophinopathies are the most common type of inherited muscular diseases. Muscle biopsy and genetic tests are effective to diagnose the disease but cost much more than primary hospitals can reach. The more available muscle MRI is promising but its diagnostic results highly depends on doctors' experiences. This study intends to explore a way of deploying a deep learning model for muscle MRI images to diagnose dystrophinopathies.
methodsThis study collected 2536 T1WI images from 432 cases who had been diagnosed by genetic analysis and/or muscle biopsy, including 148 cases with dystrophinopathies and 284 cases with other diseases. The data was randomly divided into three sets: the data from 233 cases were used to train the CNN model, the data from 97 cases for the validation experiments, and the data from 102 cases for the test experiments. We also validated our models expertise at diagnosing by comparing the model's results on the 102 cases with those of three skilled radiologists.
resultsThe proposed model achieved 91% (95% CI: 0.88, 0.93) accuracy on the test set, higher than the best accuracy of 84% in radiologists. It also performed better than the skilled radiologists in sensitivity : sensitivities of the models and the doctors were 0.89 (95% CI: 0.85 0.93) versus 0.79 (95% CI:0.73, 0.84; p = 0.190).
conclusionsThe deep model achieved excellent accuracy and sensitivity in identifying cases with dystrophinopathies. The comparable performance of the model and skilled radiologists demonstrates the potential application of the model in diagnosing dystrophinopathies through MRI images.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.