Digital Twin and Ml-basEd MOdel of TEVAR Interventions

The study aims to collect clinical data and pseudonymized CT images of patients undergoing TEVAR in order to create an anatomical digital twin capable of simulating procedural outcomes and training machine learning (ML) algorithms. This approach will support predictive models that may assist physicians in selecting the optimal medical device, improving pre-TEVAR planning, and predicting post-TEVAR complications.

Inclusion Criteria: * ≥18 Years and older (Adult, Older Adult) * Female and male * Received TEVAR for: Chronic or acute dissection, Aneurysm, Penetrating aortic ulcer, aortic thrombus, intramural hematoma or traumatic injury Exclusion Criteria: * Younger than 18 years old * Received TEVAR in surgical graft that replaced native aorta * Poor CT image quality that leads to failure in generating a high-fidelity 3D FE model of patient anatomy (no preoperative multidetector contrast-enhanced CT-scan available, preoperative CTscan slice thickness greater than 1mm, preoperative CT-scan with artifacts, motion artifacts due to the presence of other implanted devices affecting the region of interest)