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- Title
Automated detection of incidental abdominal aortic aneurysms on computed tomography.
- Authors
Chatterjee, Devina; Shen, Thomas C.; Mukherjee, Pritam; Lee, Sungwon; Garrett, John W.; Zacharias, Nicholas; Pickhardt, Perry J.; Summers, Ronald M.
- Abstract
Purpose: To detect and assess abdominal aortic aneurysms (AAAs) on CT in a large asymptomatic adult patient population using fully-automated deep learning software. Materials and methods: The abdominal aorta was segmented using a fully-automated deep learning model trained on 66 manually-segmented abdominal CT scans from two datasets. The axial diameters of the segmented aorta were extracted to detect the presence of AAAs—maximum axial aortic diameter greater than 3 cm were labeled as AAA positive. The trained system was then externally-validated on CT colonography scans of 9172 asymptomatic outpatients (mean age, 57 years) referred for colorectal cancer screening. Using a previously-validated automated calcified atherosclerotic plaque detector, we correlated abdominal aortic Agatston and volume scores with the presence of AAA. Results: The deep learning software detected AAA on the external validation dataset with a sensitivity, specificity, and AUC of 96%, (95% CI 89%, 100%), 96% (96%, 97%), and 99% (98%, 99%) respectively. The Agatston and volume scores of reported AAA-positive cases were statistically significantly greater than those of reported AAA-negative cases (p < 0.0001). Using plaque alone as a AAA detector, at a threshold Agatston score of 2871, the sensitivity and specificity were 84% (73%, 94%) and 87% (86%, 87%), respectively. Conclusion: Fully-automated detection and assessment of AAA on CT is feasible and accurate. There was a strong statistical association between the presence of AAA and the quantity of abdominal aortic calcified atherosclerotic plaque.
- Subjects
ABDOMINAL aortic aneurysms; COMPUTED tomography; DEEP learning; ABDOMINAL aorta; ATHEROSCLEROTIC plaque; VIRTUAL colonoscopy
- Publication
Abdominal Radiology, 2024, Vol 49, Issue 2, p642
- ISSN
2366-004X
- Publication type
Article
- DOI
10.1007/s00261-023-04119-1