Fully automatic four-module deep learning pipeline for identifying, segmenting, and Stanford-subtyping aortic dissection (AD) from CT angiography (CTA). A 3D full-resolution nnU-Net first segments the aorta; the segmented boundary is then multi-view projected for AD identification; for AD-positive cases, a second 3D nnU-Net segments the true lumen (TL) and false lumen (FL); finally, a classifier performs Stanford subtyping from multi-view maximum-density projections of the TL/FL. On 386 CTA scans, the pipeline achieved 0.979 accuracy for AD identification, Dice of 0.968 (TL) and 0.971 (FL) for lumen segmentation, and 0.990 accuracy for Stanford subtyping.

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.

Deep learning pipeline for detection and quantification of abdominal aortic aneurysm (AAA) -- a typically asymptomatic condition often missed incidentally by radiologists -- from abdominal-pelvic CT. A modified 3D U-Net segments the aorta on both contrast and non-contrast CT volumes with a variable number of images, after which an ellipse-fitting post-processing step measures the aortic cross-sectional diameter along the vessel to detect aneurysmal dilation. Trained and validated on 321 abdominal-pelvic CT examinations from Massachusetts General Hospital, the model reached a sensitivity/specificity of 0.91/0.95 on the primary validation set, and 0.85/1.0 on a separate 57-exam generalization test set with different patient demographics and acquisition characteristics; the authors report that DeepAAA exceeded literature-reported radiologist performance for incidental AAA detection.

Dilated U-Net model for fully automatic segmentation of the intima-media complex (IMC) of the common carotid artery on longitudinal B-mode ultrasound images, used to measure carotid intima-media thickness (cIMT) -- a standard imaging biomarker of subclinical atherosclerosis. A far-wall detection step first localizes the region of interest, and the dilated U-Net then segments the near- and far-wall IMC boundaries within it. Trained and evaluated with 5-fold cross-validation on a multicenter database of 2,176 images annotated by two experts, the method reached a mean absolute thickness difference of under 120 micrometres versus the reference annotations -- smaller than the approximately 180-micrometre inter-observer variability -- with a 98.7% fully-automatic success rate (only 1.3% of cases required manual correction).