Quantification of the Thoracic Aorta and Detection of Aneurysm at CT: Development and Validation of a Fully Automatic Methodology.

Purpose: To develop and validate a deep learning-based system that predicts the largest ascending and descending aortic diameters at chest CT through automatic thoracic aortic segmentation and identifies aneurysms in each segment. Materials and Methods: In this retrospective study conducted from July 2019 to February 2021, a U-Net and a postprocessing algorithm for thoracic aortic segmentation and measurement were developed by using a dataset (dataset A) that included 315 CT studies split into training, hyperparameter-tuning, and testing sets. The U-Net and postprocessing algorithm were associated with a Digital Imaging and Communications in Medicine series filter and visualization interface and were further validated by using a dataset (dataset B) that included 1400 routine CT studies. In dataset B, system-predicted measurements were compared with annotations made by two independent readers as well as radiology reports to evaluate system performance. Results: In dataset B, the mean absolute error between the automatic and reader-measured diameters was equal to or less than 0.27 cm for both the ascending aorta and the descending aorta. The intraclass correlation coefficients (ICCs) were greater than 0.80 for the ascending aorta and equal to or greater than 0.70 for the descending aorta, and the ICCs between readers were 0.91 (95% CI: 0.90, 0.92) and 0.82 (95% CI: 0.80, 0.84), respectively. Aneurysm detection accuracy was 88% (95% CI: 86, 90) and 81% (95% CI: 79, 83) compared with reader 1 and 90% (95% CI: 88, 91) and 82% (95% CI: 80, 84) compared with reader 2 for the ascending aorta and descending aorta, respectively. Conclusion: Thoracic aortic aneurysms were accurately predicted at CT by using deep learning.