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Objectives: This review aims to review the application of deep learning (DL) techniques in the imaging analysis of abdominal aortic aneurysm (AAA), with a specific focus on the segmentation of intraluminal thrombus (ILT). Methods: A comprehensive literature review was conducted through searches of PUBMED and Web of Science up to September 2025. Only English-language studies applying DL-based networks for ILT segmentation in patients with AAA on computed tomography angiography were included. After screening 664 articles, 22 met the eligibility criteria and were included. The reported methodological frameworks and segmentation performance metrics were extracted for comparison and analysis. Results: Among the studies included, the reported Dice similarity coefficients ranged from 0.81 to 0.93 for 2D networks and from 0.804 to 0.9868 for 3D networks. Notably, 2D Multiview fusion models outperform other 2D approaches, while 3D U-Net remains a strong baseline. Methods using preoperative images demonstrated great applicability for surgical planning, while postoperative segmentation faced challenges related to imaging artifacts caused by stent. Conclusions: This review provides a comprehensive overview of recent DL-based ILT segmentation methods for AAA patients on CTA, offering perspectives for applications in advanced preoperative planning and postoperative surveillance. Despite the promising results, the lack of standardized datasets limits model development and external validation. Future research should address these limitations by focusing on multicenter standardized datasets and seamless integration into clinical workflows.
Goffart et al. (Sun,) studied this question.