ArticleAbdominal radiology (New York)2025
Methodology for a fully automated pipeline of AI-based body composition tools for abdominal CT.
Article in Abdominal radiology (New York), 2025. 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.
- Future of CT body composition research: Methodological discrepancies and advances.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2026Review
- Cloud-Enabled Automated CT Assessment of Pelvic Muscle Quality in Women With and Without Low-Energy Femoral Neck Fracture.Calcified tissue international · 2026Article
- Computed Tomography-Derived visceral fat area is associated with increased risk of kidney stone recurrence.Urolithiasis · 2026Article
- Automated CT-Based Muscle Density Predicts Mortality Regardless of Muscle Area.Journal of cachexia, sarcopenia and muscle · 2026Article
- Ratio of visceral-to-subcutaneous fat area improves long-term mortality prediction over either measure alone: automated CT-based AI measures with longitudinal follow-up in a large adult cohort.Abdominal radiology (New York) · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Accurate, reproducible body composition analysis from abdominal computed tomography (CT) images is critical for both clinical research and patient care. We present a fully automated, artificial intelligence (AI)-based pipeline that streamlines the entire process-from data normalization and anatomical landmarking to automated tissue segmentation and quantitative biomarker extraction. Our methodology ensures standardized inputs and robust segmentation models to compute volumetric, density, and cross-sectional area metrics for a range of organs and tissues. Additionally, we capture selected DICOM header fields to enable downstream analysis of scan parameters and facilitate correction for acquisition-related variability. By emphasizing portability and compatibility across different scanner types, image protocols, and computational environments, we ensure broad applicability of our framework. This toolkit is the basis for the Opportunistic Screening Consortium in Abdominal Radiology (OSCAR) and has been shown to be robust and versatile, critical for large multi-center studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.