A Scoping Review on Artificial Intelligence for Image-Based Analysis in Aortic Aneurysm Care

Artificial intelligence (AI) has emerged as a promising approach to enhance automated image analysis, quantitative assessment, and clinical decision-making in aortic aneurysm care. This scoping review aims to summarize the current cumulative body of evidence.

A systematic PubMed search identified 661 records, with 59 studies ultimately included after de-duplication and multi-stage screening based on predefined eligibility criteria, focusing on original research applying artificial intelligence to medical imaging in aortic aneurysm care. Data were extracted on study design, imaging modality, AI methodology, validation, and dataset characteristics.

Fifty-nine studies were included. Deep learning approaches, particularly U-Net-based architectures, were the most frequently applied methods, with AI primarily used for automated segmentation and quantitative analysis of aortic pathology. AI models achieved high technical performance across applications, including vessel segmentation, aneurysm detection, endoleak identification, and risk prediction, with reported improvements in efficiency and diagnostic accuracy. However, external validation, multimodal data integration, and prospective clinical evaluation remained uncommon.

AI demonstrates substantial potential to enhance aortic aneurysm disease assessment through accurate image analysis, automated quantification, and improved procedural support. However, current limitations include lack of external validation, reporting transparency, and clinical integration.