KI-gestützte EVAR-Planung – Erfahrungen in einfachen und komplexen Fällen mit der Digitalen Zwilling-Technologie

Background: The aim of this study is to present initial experiences with AI-assisted EVAR planning in cases of different complexity, and to discuss its potential clinical benefits, current limitations, and future development perspectives. Patients/Materials and Methods: Starting from March 2024, this single centre study included prospectively the first infrarenal and complex EVAR cases that were planned with additional preoperative simulation using the AI-based Digital Twin technology (Predisurge SAS, St. Etienne, France). The study assessed the concordance between simulation results, clinical planning, and postoperative outcomes, focusing on the occurrence of Type Ia endoleaks after infrarenal EVAR and target vessel complications during the implantation of fenestrated/branched endografts (F/BEVAR). Additionally, the potential impact of the simulation on therapeutic decision-making was evaluated. Results: 9 infrarenal EVAR cases (all male, median age 74 [61-80] years) and 10 F/BEVAR cases (4 male, median age 73 [58-82] years) were included. 7/9 patients with infrarenal pathology were treated with Endurant IIs endografts (Medtronic, Dublin, Ireland), one patient with a Zenith Alpha endograft (Cook Medical, Australia Pty Ltd., Brisbane, Australia), and one patient with an Anaconda endograft (Terumo Aortic, Inchinnan, Scotland). In 2 cases, a larger main body was selected based on the simulation results. In 3 cases, the simulation indicated an elevated Endoleak Risk Index (ERI), although no Type Ia endoleak was observed during follow-up; in these cases, the simulation results differed from the surgeon's assessment. Among the F/BEVAR patients (7/10 Cook Medical, 3/10 Terumo Aortic), the simulation led to a change in graft design or fenestration position (1/10 each) and predicted target vessel complications in three cases. Conclusion: AI-assisted EVAR planning is still in its early stages but offers numerous opportunities for further development, which are eagerly anticipated. Initial clinical experiences are promising; however, larger studies are needed to confirm and validate these findings. It is expected that preoperative AI-assisted EVAR simulation will play a significant role in the future - not only in clinical decision-making for both simple and complex cases, but also in education and training.