Clinical Evidence of Endoleak Risk Index (ERI)

To validate the ERI in a clinical setting, a multi-centric retrospective study — EnduSim— was conducted across several European centers. Consecutive patients who underwentEVAR with Medtronic Endurant II devices were analysed and subsequently divided into two groups:  

• ‍Patients with type Ia Endoleak (EL1A): All patients who developed a type Ia Endoleak after EVAR, regardless of when the complication occurred. ‍
• Control patients: Patients without type Ia Endoleak and with at least three years of follow-up were.

For all patients, simulation of EVAR implantation was performed in the same way as the real procedure.

First Version of ERI

The performance of the first version of ERI was published by Derycke et al. (EurJ Vasc Endovasc Surg, 2024). At that time, machine learning was not yet integrated into the algorithm.

The study showed that conventional sizing parameters — including those recommended in the device’s Instructions for Use (IFU) — did not differ significantly between patients with and without type Ia Endoleak. In contrast, almost all digital twin–derived parameters showed significant differences between the two groups.

Current Version of ERI

The present version of the ERI algorithm integrates machine learning, which the current dataset includes 117 patients in the training set (83 controls and 34 with type Ia endoleak — 13 early and 21 late) and 56 patients in the validation set (36 controls and 20 with type Ia endoleak — 6 early and 14 late).

Preliminary validation results demonstrate strong performance:

• Sensitivity: 80% (16 out of 20 patients with EL1A correctly identified). ‍
• Specificity: 83% (30 out of 36 control patients correctly identified). 

These findings confirm that the ERI can reliably distinguish high-risk patients from low-risk cases, offering clinicians a decision-support tool for EVAR planning.

Clinical Evidence

ERI algorithm is a machine learning model based on meaningful physical features such as oversizing, proximal neck shape, conicity, apposition, presence of thrombus, and derived parameters from the previous lists. These features have been selected because they characterise the proximal sealing zone and play a role in assessing the risk of type IA Endoleak. ERI combines the most relevant features derived from the simulation with digital twin into a single easy-to-understand risk index.

• Cho S, Kim H, Joh J. Digital Twin and Artificial Intelligence Technologies to Assess the Type IA Endoleak. Bioengineering (Basel). 2025 Dec 19;13(1):1. doi: 10.3390/bioengineering13010001. PMID: 41595934; PMCID: PMC12837158.
• Keschenau PR, Döring M, Elshafei S, Palacios D, Stark M, Ghazal M, Albertini JN, Kalder J. Early clinical experiences with AI-based EVAR planning using the Endoleak Risk Index support its value for individualized decision-making and education. J Vasc Surg Cases Innov Tech. 2025 Oct 30;12(1):102037. doi: 10.1016/j.jvscit.2025.102037. PMID: 41403761; PMCID: PMC12704054.