ArticleAcademic radiology2019
Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans.
Article in Academic radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Weighted vest use or resistance exercise to offset muscle loss in older adults: secondary findings from the INVEST in bone health RCT.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Trial
- Musculoskeletal characteristics in older adults with overweight or obesity: INVEST in Bone Health trial baseline analysis.Obesity (Silver Spring, Md.) · 2025Trial
- Automated Muscle Measurement on Chest CT Predicts All-Cause Mortality in Older Adults From the National Lung Screening Trial.The journals of gerontology. Series A, Biological sciences and medical sciences · 2021Trial
- A novel multimodal AI-based radiomics approach for precision assessment of pain intensity in chronic nonspecific low back pain.Journal of orthopaedic translation · 2026Article
- Association of computed tomography (CT)-derived muscle area and density at multiple sites with 10-year fracture risk in older men.JBMR plus · 2026Article
- Associations of CT Muscle Area and Density With Functional Outcomes and Mortality Across Anatomical Regions in Older Men.Journal of the American Geriatrics Society · 2025Article
- Deep Learning Technique for Automatic Segmentation of Proximal Hip Musculoskeletal Tissues From CT Scan Images: A MrOS Study.Journal of cachexia, sarcopenia and muscle · 2025Article
- Assessment of trabecular bone Hounsfield units in the lumbar spine for osteoporosis evaluation in individuals aged 65 and above: a review.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Review
- Analysis of the current status of computed tomography diagnosis of sarcopenia.Archives of medical science : AMS · 2025Article
- Prognostic utility of paraspinal muscle index in elderly patients with community-acquired pneumonia.Clinical and experimental emergency medicine · 2024Article
- Association of CT-Derived Skeletal Muscle and Adipose Tissue Metrics with Frailty in Older Adults.Academic radiology · 2024Article
- Body composition assessment with ultrasound muscle measurement: optimization through the use of semi-automated tools in colorectal cancer.Frontiers in nutrition · 2024Article
- Opportunistic screening for osteoporosis by abdominal CT in a British population.Insights into imaging · 2023Article
- Segmentation of multi-regional skeletal muscle in abdominal CT image for cirrhotic sarcopenia diagnosis.Frontiers in neuroscience · 2023Article
- The association between morphological characteristics of paraspinal muscle and spinal disorders.Annals of medicine · 2023Review
- Ultrasound and magnetic resonance imaging as diagnostic tools for sarcopenia in immune-mediated rheumatic diseases (IMRDs).La Radiologia medica · 2022Review
- Sarcopenia: imaging assessment and clinical application.Abdominal radiology (New York) · 2022Review
- Determination of Low Muscle Mass by Muscle Surface Index of the First Lumbar Vertebra Using Low-Dose Computed Tomography.Journal of clinical medicine · 2022Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
RATIONALE AND
objectivesTo develop and evaluate an automated machine learning (ML) algorithm for segmenting the paraspinous muscles on chest computed tomography (CT) scans to evaluate for presence of sarcopenia. MATERIALS AND
methodsA convolutional neural network based on the U-Net architecture was trained to perform muscle segmentation on a dataset of 1875 single slice CT images and was tested on 209 CT images of participants in the National Lung Screening Trial. Low-dose, noncontrast CT examinations were obtained at 33 clinical sites, using scanners from four manufacturers. The study participants had a mean age of 71.6 years (range, 70-74 years). Ground truth was obtained by manually segmenting the left paraspinous muscle at the level of the T12 vertebra. Muscle cross-sectional area (CSA) and muscle attenuation (MA) were recorded. Comparison between the ML algorithm and ground truth measures of muscle CSA and MA were obtained using Dice similarity coefficients and Pearson correlations.
resultsCompared to ground truth segmentation, the ML algorithm achieved median (standard deviation) Dice scores of 0.94 (0.04) in the test set. Mean (SD) muscle CSA was 14.3 (3.6) cm
conclusionThe ML algorithm for measurement of paraspinous muscles compared favorably to manual ground truth measurements in the NLST. The algorithm generalized well to a heterogeneous set of low-dose CT images and may be capable of automated quantification of muscle metrics to screen for sarcopenia on routine chest CT examinations.
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