Unsupervised machine learning for identifying morphological phenotypes in abdominal aortic aneurysms using fully automated volume-segmented imaging: a multicentre cohort study.

Aims: Thrombo- and microembolic complications following abdominal aortic aneurysm (AAA) repair are hypothesized to be associated with wall thrombus burden. Fully automatic volume segmentation (FAVS) of imaging enables extraction of morphological features from which thrombogenic phenotypes may be identified. Methods and results: This was a multi-centre retrospective cohort study using FAVS to examine pre-operative imaging for elective AAA repairs (2013-23). Radiological data were matched with National Vascular Registry thromboembolic outcomes data (cerebral, bowel, renal or limb ischaemia). Principal component analysis was used for dimensionality reduction, followed by unsupervised machine learning with Conclusion: Unsupervised machine learning can identify distinct aneurysm morphological phenotypes with significant thrombus burden difference, which exhibit sex imbalance. While thromboembolic events were infrequent and did not differ significantly between clusters, these anatomical phenotypes may provide a framework for future studies investigating embolic risk and sex-specific disease mechanisms.