Assessing the diagnostic accuracy of artificial intelligence in post-endovascular aneurysm repair endoleak detection using dual-energy computed tomography angiography.

Abstract

Purpose: The aim of this study was to evaluate the diagnostic accuracy of an artificial intelligence (AI) tool in detecting endoleaks in patients undergoing endovascular aneurysm repair (EVAR) using dual-energy computed tomography angiography (CTA).

Material and methods: The study involved 95 patients who underwent EVAR and subsequent CTA follow-up. Dualenergy scans were performed, and images were reconstructed as linearly blended (LB) and 40 keV virtual monoenergetic (VMI) images. The AI tool PRAEVAorta^®2 was used to assess arterial phase images for endoleaks. Two experienced readers independently evaluated the same images, and their consensus served as the reference standard. Key metrics, including accuracy, precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve (AUC), were calculated.

Results: The final analysis included 94 patients. The AI tool demonstrated an accuracy of 78.7%, precision of 67.6%, recall of 10 71.9%, F1 score of 69.7%, and an AUC of 0.77 using LB images. However, the tool failed to process 40 keV VMI images correctly, limiting further analysis of these datasets.

Conclusions: The AI tool showed moderate diagnostic accuracy in detecting endoleaks using LB images but failed to achieve the reliability needed for clinical use due to the significant number of misdiagnoses.