Open-source, fully automated framework for end-to-end aortic analysis from CT angiography (CTA), built on top of TotalSegmentator baseline segmentations with additional refinement. AortaExplorer extracts established biomarkers such as diameters across anatomical segments defined by the European Society of Cardiology, and introduces new metrics including aortic tortuosity. Diameter measurements were validated against expert manual readings in more than 10,000 CTA scans from Danish population cohorts, and the tortuosity index's increase with age is consistent with prior literature; the tool reduces per-case analysis time from about 15 minutes to under 5 minutes.

nnU-Net-based pipeline for automated detection and sub-classification of acute thoracic aortic dissection (AD) on heterogeneous CT imaging, formulated as a semantic segmentation task rather than direct image classification. The model segments the false lumen (ascending and descending) and the dissection membrane -- along with optional indirect signs such as hemopericardium, aortic wall hematoma, and supra-aortic branch dissection -- and a patient is classified as AD-positive if at least two of the three primary segmented regions exceed a volume threshold tuned via Youden's index; the same pipeline additionally flags Stanford type A dissections. Trained on 157 heterogeneous internal CT studies (not restricted to a single contrast protocol) from Mannheim University Medical Centre and evaluated on an internal held-out test set as well as public external datasets (ImageTBAD and AVT), the model reached an AUROC of 98.7% internally and 97.0% externally, and correctly flagged 93.3% of dissection cases that had not been clinically suspected before imaging. The authors state the trained network will be made publicly available as a non-medical device for further scientific research.

U-Net-variant segmentation model that identifies and quantifies coronary artery calcium (CAC) directly from routine non-gated, non-contrast chest CT scans -- the kind ordered for lung-cancer screening or unrelated indications rather than a dedicated cardiac scan -- so that the tens of millions of such scans performed annually can be opportunistically screened for cardiovascular risk without any extra imaging. Predicted calcium masks are combined with the CT's Hounsfield units to compute an Agatston-equivalent score. Trained on 446 expert-segmented scans from 98 medical centers across the U.S. Department of Veterans Affairs national health system (capturing substantial heterogeneity in scanners and protocols) and benchmarked against 795 patients with a paired same-year gated CAC study: nongated AI-CAC differentiates zero-vs-nonzero and <100-vs->=100 Agatston categories with 89.4% (F1 0.93) and 87.3% (F1 0.89) accuracy respectively, and its score stratifies 10-year all-cause mortality (CAC 0 vs. >400: 25.4% vs. 60.2%, hazard ratio 3.49) and composite stroke/MI/death risk (33.5% vs. 63.8%, hazard ratio 3.00). In a simulated opportunistic-screening run across 8,052 low-dose CT scans, cardiologists confirmed 99.2% of patients flagged with AI-CAC >400 would benefit from lipid-lowering therapy. Code and trained model weights are both public under an MIT license.