Deep learning based volumetric analysis of infrarenal abdominal aortic aneurysms characterized on CTA.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41519965.
- Also identified by DOI 10.1038/s41746-025-02262-1 and PMC identifier 12852869.
- Licence recorded as CC BY-NC-ND.
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Abstract
Volumetric assessment of abdominal aortic aneurysms (AAA) offers precise pre- and post-endovascular aortic repair (EVAR) evaluation but is laborious. The primary aim was to train and validate a network facilitating automated segmentation and volume determination of pre- and post-EVAR infrarenal AAAs displayed on computed tomography angiographies (CTA). Secondary aim was evaluation of workflow acceleration. Model was trained on ground truth segmentations. Internal and external validation was performed. AI-generated volumes of total aneurysm, lumen, and thrombus were correlated with ground truth. Model-enabled efficiency gains and semi-automatic AAA segmentations performed by three surgeons were measured. For total aneurysm, mean Dice similarity coefficient was 0.972 ± 0.013 and 0.960 ± 0.035 in internal and external validation. AI-generated thrombus volumes showed a very strong correlation with ground truth in internal (r = 0.996) and external validation (r = 0.940). Mean algorithm-facilitated time savings of 117.1 seconds (56.0%) were demonstrated for total aneurysm. Our institution-agnostic network enables automated volumetric analysis of AAAs.