ArticleJournal of the American Medical Informatics Association : JAMIA2021
Quantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Reference-Label Construction and Annotation Reporting in CT- and MRI-Based Adipose Tissue Segmentation: A Systematic Review and Evidence Map.Journal of imaging informatics in medicine · 2026Review
- Hepatic and abdominal adiposity in type 2 diabetes as assessed with machine learning on computed tomography scans.Diabetes, obesity & metabolism · 2026Article
- Sex-Based Differences in Imaging-Derived Body Composition and Their Association with Clinical Malnutrition in Abdominal Surgery Patients.Nutrients · 2026Article
- From promise to practice: a scoping review of AI applications in abdominal radiology.Abdominal radiology (New York) · 2026Article
- Reduced order computational fluid dynamic simulations in the thoracic aorta are associated with disease recorded in a medical biobank.Scientific reports · 2025Article
- A Cloud-Based System for Automated AI Image Analysis and Reporting.Journal of imaging informatics in medicine · 2025Article
- Deep metabolic phenotyping of humans with protein-altering variants inJHEP reports : innovation in hepatology · 2025Article
- CT imaging-derived phenotypes for abdominal muscle and their association with age and sex in a medical biobank.Scientific reports · 2024Article
- Abdominal Body Composition Reference Ranges and Association With Chronic Conditions in an Age- and Sex-Stratified Representative Sample of a Geographically Defined American Population.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Article
- Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning.Insights into imaging · 2024Article
- Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis.Scientific reports · 2024Article
- Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.Radiology · 2024Review
- SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification.Predictive Intelligence in Medicine. PRIME (Workshop) · 2023Article
- Automatic segmentation of large-scale CT image datasets for detailed body composition analysis.BMC bioinformatics · 2023Article
- Determination of lower radiation dose limit for automatic measurement of adipose tissue.Journal of applied clinical medical physics · 2023Article
- Relationships between body fat distribution and metabolic syndrome traits and outcomes: A mendelian randomization study.PloS one · 2023Article
- A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation.Nature genetics · 2022Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
objectiveThe objective was to develop a fully automated algorithm for abdominal fat segmentation and to deploy this method at scale in an academic biobank. MATERIALS AND
methodsWe built a fully automated image curation and labeling technique using deep learning and distributive computing to identify subcutaneous and visceral abdominal fat compartments from 52,844 computed tomography scans in 13,502 patients in the Penn Medicine Biobank (PMBB). A classification network identified the inferior and superior borders of the abdomen, and a segmentation network differentiated visceral and subcutaneous fat. Following technical evaluation of our method, we conducted studies to validate known relationships with visceral and subcutaneous fat.
resultsWhen compared with 100 manually annotated cases, the classification network was on average within one 5-mm slice for both the superior (0.4 ± 1.1 slice) and inferior (0.4 ± 0.6 slice) borders. The segmentation network also demonstrated excellent performance with intraclass correlation coefficients of 1.00 (P < 2 × 10-16) for subcutaneous and 1.00 (P < 2 × 10-16) for visceral fat on 100 testing cases. We performed integrative analyses of abdominal fat with the phenome extracted from the electronic health record and found highly significant associations with diabetes mellitus, hypertension, and renal failure, among other phenotypes.
conclusionsThis work presents a fully automated and highly accurate method for the quantification of abdominal fat that can be applied to routine clinical imaging studies to fuel translational scientific discovery.
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