ArticleMedical & biological engineering & computing2026
AortaExplorer: AI-driven analysis of the aorta in CT images.
Article in Medical & biological engineering & computing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Automated Aortic Quantification Based on Artificial Intelligence: Validation Using Contrast-Enhanced and Non-Contrast CT Scans from the Same Session.Bioengineering (Basel, Switzerland) · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce AortaExplorer, an open-source, fully automated AI-driven framework for end-to-end aortic analysis from computed tomography angiography (CTA) scans. AortaExplorer extracts established biomarkers, such as diameters across anatomical segments, and introduces novel metrics, including aortic tortuosity. Diameter measurements were validated against expert manual readings in more than 10,000 CTA scans from Danish population cohorts, demonstrating high accuracy. The computed descending aortic tortuosity index aligns with trends reported in previous studies, confirming its increase with age. By reducing analysis time from approximately 15 minutes per case to under five minutes, AortaExplorer enables efficient, reproducible assessment of aortic morphology at scale. This tool supports clinical research and large cohort studies by combining state-of-the-art segmentation performance with extensive visualization and quantitative reporting.
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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.