Anatomical deformation prediction using artificial intelligence for fusion imaging during aortic endovascular procedures

Placement of an aortic stent graft induces anatomical deformations, particularly at the iliac level, because of the introduction of rigid devices. Anticipating these changes may improve the accuracy of fusion guidance during endovascular procedures. In this prospective single-center study, a deep learning-based sizing software was used to generate a deformation-adjusted three-dimensional fusion mask, which was compared with a conventional, nondeformed mask using intraoperative angiography as the reference standard. In a cohort of 31 patients, the deformation-adjusted mask significantly improved the fusion accuracy for renal artery ostia positioning, iliac artery alignment, and guidewire localization, without any additional acquisition time. These findings suggest that deformation-aware fusion masks may enhance intraoperative navigation during aortic endovascular repair.