ArticleAcademic radiology2021
Atherosclerotic Plaque Burden on Abdominal CT: Automated Assessment With Deep Learning on Noncontrast and Contrast-enhanced Scans.
Article in Academic radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 43 citations in OpenAlex.
- Automated CT-Based Muscle Density Predicts Mortality Regardless of Muscle Area.Journal of cachexia, sarcopenia and muscle · 2026Article
- Quantification of Abdominal Aortic Calcification on CT: Clinical Validation for Assessment Of Cardiovascular Risk in Oncology.Journal of imaging informatics in medicine · 2026Article
- From promise to practice: a scoping review of AI applications in abdominal radiology.Abdominal radiology (New York) · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Generating Training Data for Ureter Segmentation Using Dual-Energy CT Two-Material Decomposition.Journal of imaging informatics in medicine · 2026Article
- Tumor-Derived Polyamines Initiate Fat Wasting in Cancer Cachexia.bioRxiv : the preprint server for biology · 2025Article
- Opportunistic assessment of abdominal aortic calcification using artificial intelligence (AI) predicts coronary artery disease and cardiovascular events.American heart journal · 2025Article
- Comparing fully automated AI body composition biomarkers at differing virtual monoenergetic levels using dual-energy CT.Abdominal radiology (New York) · 2025Article
- Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity.Nature communications · 2025Article
- Independent Associations of Aortic Calcification with Cirrhosis and Liver Related Mortality in Veterans with Chronic Liver Disease.Digestive diseases and sciences · 2024Article
- Adjusting for the effect of IV contrast on automated CT body composition measures during the portal venous phase.Abdominal radiology (New York) · 2024Article
- AI-generated CT body composition biomarkers associated with increased mortality risk in socioeconomically disadvantaged individuals.Abdominal radiology (New York) · 2024Article
- Comparing fully automated AI body composition measures derived from thin and thick slice CT image data.Abdominal radiology (New York) · 2024Article
- Fully-automated multi-organ segmentation tool applicable to both non-contrast and post-contrast abdominal CT: deep learning algorithm developed using dual-energy CT images.Scientific reports · 2024Article
- Automated Plaque Detection and Agatston Score Estimation on Non-Contrast CT Scans: A Multicenter Study.ArXiv · 2024Article
- Automated detection of incidental abdominal aortic aneurysms on computed tomography.Abdominal radiology (New York) · 2024Article
- Reference distributions of aortic calcification and association with Framingham risk score.Scientific reports · 2023Article
- Validation of a Length-Adjusted Abdominal Arterial Calcium Score Method for Contrast-Enhanced CT Scans.Diagnostics (Basel, Switzerland) · 2023Article
- Opportunistic Screening:Radiology · 2023Review
- Abdominal CT Body Composition Thresholds Using Automated AI Tools for Predicting 10-year Adverse Outcomes.Radiology · 2023Article
Corrections and comments
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Authors and funding
15 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundAbdominal aortic atherosclerotic plaque burden may have clinical significance but manual measurement is time-consuming and impractical. PURPOSE: To perform external validation on an automated atherosclerotic plaque detector for noncontrast and postcontrast abdominal CT. MATERIALS AND
methodsThe training data consisted of 114 noncontrast CT scans and 23 postcontrast CT urography scans. The testing data set consisted of 922 CT colonography (CTC) scans, and 1207 paired noncontrast and postcontrast CT scans from renal donors from a second institution. Reference standard data included manual plaque segmentations in the 137 training scans and manual plaque burden measurements in the 922 CTC scans. The total Agatston score and group (0-3) was determined using fully-automated deep learning software. Performance was assessed by measures of agreement, linear regression, and paired evaluations.
resultsOn CTC scans, automated Agatston scoring correlated highly with manual assessment (R
conclusionFully automated detection, segmentation, and scoring of abdominal aortic atherosclerotic plaques on both pre- and post-contrast CT was validated and may have application for population-based 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 OpenQuestion graph.