AortaExplorer: AI-driven analysis of the aorta in CT images

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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Publication Details

Journal
Medical & Biological Engineering & Computing
Published
2026-03-16
DOI
https://doi.org/10.1007/s11517-026-03535-x
Primary Topic
Aortic Disease and Treatment Approaches
Type
article
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article

AortaExplorer: AI-driven analysis of the aorta in CT images

Josefine Vilsbøll Sundgaard, Rasmus R. Paulsen, Michael Huy Cuong Pham, Kristine Aavild Sørensen et al.
Medical & Biological Engineering & Computing
Aortic Disease and Treatment Approaches
article

AortaExplorer: AI-driven analysis of the aorta in CT images

Josefine Vilsbøll Sundgaard, Rasmus R. Paulsen, Michael Huy Cuong Pham, Kristine Aavild Sørensen, Jørgen Tobias Kühl, Klaus Fuglsang Kofoed, Linnea Hjordt Juul
article en

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.

Medical & Biological Engineering & Computing
University of Copenhagen (DK), Novo Nordisk (Denmark) (DK), Copenhagen University Hospital (DK), Rigshospitalet (DK), Technical University of Denmark (DK)
Quality Education
Openalex Percentile: Top 20%
Aortic Disease and Treatment Approaches
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AortaExplorer: AI-driven analysis of the aorta in CT images — Josefine Vilsbøll Sundgaard, Rasmus R. Paulsen, et al. · Medical & Biological Engineering & Computing (2026) | TGRS Research Map | TGRS