ArticleDiagnostics (Basel, Switzerland)2021
Automated Screening for Abdominal Aortic Aneurysm in CT Scans under Clinical Conditions Using Deep Learning.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Computer vision applications in vascular surgery: a systematic review and critical appraisal.NPJ digital medicine · 2026Article
- Insertion Network for Image Sequence Correspondence Building.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Abdominal Aortic Aneurysm Pathomechanics, Current Understanding and Emerging Directions: A Perspective for 2025 and Beyond.Advances in experimental medicine and biology · 2026Review
- A novel morphological assessment approach for abdominal aortic aneurysms: automated segmentation and measurement using a semi-supervised deep learning algorithm.Frontiers in cardiovascular medicine · 2026Article
- Deep learning algorithm for predicting rapid progression of abdominal aortic aneurysm by integrating CT images and clinical features.Scientific reports · 2025Article
- Streamlining Acute Abdominal Aortic Dissection Management-An AI-based CT Imaging Workflow.Journal of imaging informatics in medicine · 2024Article
- Review
- Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm.Frontiers in cardiovascular medicine · 2024Review
- Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography.Journal of digital imaging · 2023Article
- Artificial intelligence in clinical workflow processes in vascular surgery and beyond.Seminars in vascular surgery · 2023Review
- Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review.Frontiers in medicine · 2023Review
- An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology.Diagnostics (Basel, Switzerland) · 2022Review
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Authors and funding
11 authors.
Funding
Abstract
Abdominal aortic aneurysms (AAA) may remain clinically silent until they enlarge and patients present with a potentially lethal rupture. This necessitates early detection and elective treatment. The goal of this study was to develop an easy-to-train algorithm which is capable of automated AAA screening in CT scans and can be applied to an intra-hospital environment. Three deep convolutional neural networks (ResNet, VGG-16 and AlexNet) were adapted for 3D classification and applied to a dataset consisting of 187 heterogenous CT scans. The 3D ResNet outperformed both other networks. Across the five folds of the first training dataset it achieved an accuracy of 0.856 and an area under the curve (AUC) of 0.926. Subsequently, the algorithms performance was verified on a second data set containing 106 scans, where it ran fully automated and resulted in an accuracy of 0.953 and an AUC of 0.971. A layer-wise relevance propagation (LRP) made the decision process interpretable and showed that the network correctly focused on the aortic lumen. In conclusion, the deep learning-based screening proved to be robust and showed high performance even on a heterogeneous multi-center data set. Integration into hospital workflow and its effect on aneurysm management would be an exciting topic of future research.
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Registered trials
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