ArticleNature communications2023
Automated temporalis muscle quantification and growth charts for children through adulthood.
Article in Nature communications, 2023. 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.
- Architecture of lower leg muscles in children: Reference curves and potential mechanisms of growth.Journal of anatomy · 2026Article
- A generalisation study in deep learning-based segmentation of lower-limb muscles across different populations.Scientific reports · 2026Article
- Association between temporal muscle morphology based on MRI and muscle mass and strength in healthy young adults: a cross-sectional study.BMC musculoskeletal disorders · 2026Article
- TissUnet: Improved extracranial tissue and cranium segmentation for children through adulthood.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Prognostic value of temporal muscle thickness in pediatric medulloblastoma patients aged 3-12 years.Journal of neuro-oncology · 2025Article
- Machine Learning-Based Classification of ICU-Acquired Neuromuscular Weakness: A Comparative Study in Survivors of Critical Illness.Life (Basel, Switzerland) · 2025Article
- Added prognostic value of temporal muscle thickness in glioblastoma with age-stratified analysis.Scientific reports · 2025Article
- Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs.AJNR. American journal of neuroradiology · 2025Article
- Article
- Childhood muscle growth: Reference curves for lower leg muscle volumes and their clinical application in cerebral palsy.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Fostering Tomorrow: Uniting Artificial Intelligence and Social Pediatrics for Comprehensive Child Well-being.Turkish archives of pediatrics · 2024Article
- Temporal Muscle Thickness Compared to Functional Scales as a Prognostic Parameter in Patients with Brain Metastases.Cancers · 2024Article
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Authors and funding
23 authors.
Funding
Abstract
Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.
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Registered trials
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