Multicentric Clinical Evaluation of a Computed Tomography based Fully automated Deep Neural Network for Aortic Maximum Diameter and Volumetric Measurements.

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Abstract

This study aims to evaluate a fully automatic deep learning-based method (Augmented Radiology for Vascular Aneurysm [ARVA]) for aortic segmentation and simultaneous diameter and volume measurements.A clinical validation dataset was constructed from preoperative and postoperative aortic Computed Tomography Angiography (CTA) scans for assessing these functions. The dataset totalled 350 CTA scans from 216 patients treated at two different hospitals. ARVA’s ability to segment the aorta into seven morphologically based aortic segments and measure maximum outer-to-outer wall transverse diameters and compute volumes for each was compared with the measurements of six experts (ground truth) and thirteen clinicians.Ground truth (experts’) measurements of diameters and volumes were manually performed for all aortic segments. The median absolute diameter difference between ground truth and ARVA was 1.6mm ([1.5, 1.7] 95% confidence interval (CI)), and 1.6mm ([1.6, 1.7] 95% CI) between ground truth and clinicians. ARVA produced measurements within the clinical acceptable range with a proportion of 85.5% ([83.5,86.3] 95% CI) compared to the clinicians’ 86.0% ([83.9, 86.0] 95% CI). Median volume similarity error ranged from 0.93 to 0.95 in the main trunk and achieved 0.88 in the iliac arteries.This study demonstrates the reliability of a fully automated AI-driven solution capable of quick aortic segmentation and analysis of both diameter and volume for each segment.Copyright © 2024. Published by Elsevier Inc.