ArticleJournal of digital imaging2023
Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Abdominal Aortic Aneurysm Pathomechanics, Current Understanding and Emerging Directions: A Perspective for 2025 and Beyond.Advances in experimental medicine and biology · 2026Review
- Validation of a Deep Learning U-Net Algorithm for Multistructure Segmentation of Infrarenal Abdominal Aortic Aneurysms including Lumen, Thrombus, and Calcifications.EJVES vascular forum · 2026Article
- Automatic Segmentation of Intraluminal Thrombus in Abdominal Aortic Aneurysms Based on CT Images: A Comprehensive Review of Deep Learning-Based Methods.Journal of clinical medicine · 2025Review
- Deep learning algorithm for predicting rapid progression of abdominal aortic aneurysm by integrating CT images and clinical features.Scientific reports · 2025Article
- Review
- Artificial intelligence in aortic CT angiography: current applications and future perspectives.Frontiers in cardiovascular medicine · 2025Review
- Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment.Journal of cardiovascular development and disease · 2024Review
- Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm.Frontiers in cardiovascular medicine · 2024Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of our study is to validate a totally automated deep learning (DL)-based segmentation pipeline to screen abdominal aortic aneurysms (AAA) in computed tomography angiography (CTA) scans. We retrospectively evaluated 73 thoraco-abdominal CTAs (48 AAA and 25 control CTA) by means of a DL-based segmentation pipeline built on a 2.5D convolutional neural network (CNN) architecture to segment lumen and thrombus of the aorta. The maximum aortic diameter of the abdominal tract was compared using a threshold value (30 mm). Blinded manual measurements from a radiologist were done in order to create a true comparison. The screening pipeline was tested on 48 patients with aneurysm and 25 without aneurysm. The average diameter manually measured was 51.1 ± 14.4 mm for patients with aneurysms and 21.7 ± 3.6 mm for patients without aneurysms. The pipeline correctly classified 47 AAA out of 48 and 24 control patients out of 25 with 97% accuracy, 98% sensitivity, and 96% specificity. The automated pipeline of aneurysm measurements in the abdominal tract reported a median error with regard to the maximum abdominal diameter measurement of 1.3 mm. Our approach allowed for the maximum diameter of 51.2 ± 14.3 mm in patients with aneurysm and 22.0 ± 4.0 mm in patients without an aneurysm. The DL-based screening for AAA is a feasible and accurate method, calling for further validation using a larger pool of diagnostic images towards its clinical use.
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