ArticleJournal of cachexia, sarcopenia and muscle2025
Leg Muscle Volume, Intramuscular Fat and Force Generation: Insights From a Computer-Vision Model and Fat-Water MRI.
Article in Journal of cachexia, sarcopenia and muscle, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02157038 (Neuromuscular Mechanisms Underlying Poor Recovery From Whiplash Injuries), which is not on this map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neuromuscular Mechanisms Underlying Poor Recovery From Whiplash Injuries
Who cites it
6 citing papers in PubMed.
- Artificial Intelligence in Spine Neuroimaging: Diagnostic and Prognostic Utility of Novel Biomarkers in Lower Back Pain.Journal of clinical medicine · 2026Review
- Intra- and inter-rater reliability of skeletal muscle mass MRI assessment among adults: a systematic review.Quantitative imaging in medicine and surgery · 2026Review
- Deep learning-based assessment of paraspinal muscle degeneration and its relationships to muscle function and disability outcomes in chronic low back pain: a prospective study.European radiology · 2026Article
- MRI image segmentation of the major lower leg muscles using deep learning: application in biomechanical analysis.Medical & biological engineering & computing · 2026Article
- Improved Strength Prediction Combining MRI Biomarkers of Muscle Quantity and Quality.NMR in biomedicine · 2025Article
- Automated Segmentation of Forearm Muscles: Clinical Associations With Hand Function, Muscle Volume and Intramuscular Fat.JCSM communicationsArticle
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
backgroundMaintaining skeletal muscle health (i.e., muscle size and quality) is crucial for preserving mobility. Decreases in lower limb muscle volume and increased intramuscular fat (IMF) are common findings in people with impaired mobility. We developed an automated method to extract markers of leg muscle health, muscle volume and IMF, from MRI. We then explored their associations with age, body mass index (BMI), sex and voluntary force generation.
methodsWe trained (n = 34) and tested (n = 16) a convolutional neural network (CNN) to segment five muscle groups in both legs from fat-water MRI to explore muscle volume and IMF. In 95 participants (70 females, 25 males, mean age [standard deviation] = 34.2 (11.2) years, age range = 18-60 years), we explored associations between the CNN measures and age, BMI and sex, and then in a subset of 75 participants, we explored associations between CNN muscle volume, CNN IMF and maximum plantarflexion force after controlling for age, BMI and sex.
resultsThe CNN demonstrated high test accuracy (Sørensen-Dice index ≥ 0.87 for all muscle groups) and reliability (muscle volume ICC
conclusionsComputer-vision models combined with fat-water MRI permits the non-invasive, automatic assessment of leg muscle volume and IMF. Associations with age, BMI and sex are important when interpreting these measures. Markers of leg muscle health may enhance our understanding of the relationship between muscle health, force generation and mobility.
trial registrationClinicalTrials.gov identifier: NCT02157038.
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Registered trials
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