The automated deep learning-based pipeline correctly classified abdominal aortic aneurysms with 97% accuracy, 98% sensitivity, and 96% specificity compared to expert manual measurements.
Observational (n=73)
Single-blind
Yes
Does an automated deep learning-based segmentation pipeline accurately screen and measure abdominal aortic aneurysms in CTA scans compared to manual expert measurement?
A fully automated deep learning pipeline can accurately segment and measure abdominal aortic aneurysms from CTA scans with 97% accuracy compared to expert manual measurement, demonstrating feasibility for automated screening.
Effect estimate: 97% accuracy
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.
Spinella et al. (Wed,) conducted a observational in Abdominal aortic aneurysm (n=73). Deep learning-based automated segmentation pipeline vs. Manual measurement by expert vascular surgeon was evaluated on Accuracy of AAA classification (diameter > 30 mm) (97% accuracy). The automated deep learning-based pipeline correctly classified abdominal aortic aneurysms with 97% accuracy, 98% sensitivity, and 96% specificity compared to expert manual measurements.