ArticleSensors (Basel, Switzerland)2022
Concordance of Computed Tomography Regional Body Composition Analysis Using a Fully Automated Open-Source Neural Network versus a Reference Semi-Automated Program with Manual Correction.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- From Pixels to Prediction: Reviewing the Role of Artificial Intelligence in Body Composition Analysis.Journal of cachexia, sarcopenia and muscle · 2026Review
- Technical performance of L3 skeletal muscle area (SMA) measurement on CT for L3 skeletal muscle index (L3SMI) assessment.Scientific reports · 2026Article
- FocusedON-BC: A Robust Deep Learning Framework for Automated Body Composition Assessment.Nutrients · 2026Article
- Independent validation of the Mosamatic deep learning automated skeletal muscle and adipose tissue segmentation tool in an external Chinese cancer patient cohort.BJR artificial intelligence · 2026Article
- Article
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7 authors.
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Abstract
Quick, efficient, fully automated open-source programs to segment muscle and adipose tissues from computed tomography (CT) images would be a great contribution to body composition research. This study examined the concordance of cross-sectional areas (CSA) and densities for muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and intramuscular adipose tissue (IMAT) from CT images at the third lumbar (L3) between an automated neural network (test method) and a semi-automatic human-based program (reference method). Concordance was further evaluated by disease status, sex, race/ethnicity, BMI categories. Agreement statistics applied included Lin’s Concordance (CCC), Spearman correlation coefficient (SCC), Sorensen dice-similarity coefficient (DSC), and Bland−Altman plots with limits of agreement (LOA) within 1.96 standard deviation. A total of 420 images from a diverse cohort of patients (60.35 ± 10.92 years; body mass index (BMI) of 28.77 ± 7.04 kg/m2; 55% female; 53% Black) were included in this study. About 30% of patients were healthy (i.e., received a CT scan for acute illness or pre-surgical donor work-up), while another 30% had a diagnosis of colorectal cancer. The CCC, SCC, and DSC estimates for muscle, VAT, SAT were all greater than 0.80 (>0.80 indicates good performance). Agreement analysis by diagnosis showed good performance for the test method except for critical illness (DSC 0.65−0.87). Bland−Altman plots revealed narrow LOA suggestive of good agreement despite minimal proportional bias around the zero-bias line for muscle, SAT, and IMAT CSA. The test method shows good performance and almost perfect concordance for L3 muscle, VAT, SAT, and IMAT per DSC estimates, and Bland−Altman plots even after stratification by sex, race/ethnicity, and BMI categories. Care must be taken to assess the density of the CT images from critically ill patients before applying the automated neural network (test method).
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