Automated detection and quantification of aortic calcification in coronary CT angiography using deep learning: A comparative study of manual and automated scoring methods.

Abstract

Background: Aortic calcification, often incidentally detected during coronary artery calcium (CAC) scans, is underutilized in cardiovascular risk assessments due to manual quantification challenges. This study evaluates a deep learning model for automating aortic calcification detection and quantification in coronary CT angiography (CTA) images. We validate against manual assessments and compare the association of manual and automated assessments with incident major adverse cardiovascular events (MACE).

Methods: A deep learning algorithm was applied to CAC scans from 670 participants in the CORE320 and CORE64 studies. Aortic calcification in the aortic root, ascending, and descending aorta was quantified manually and automatically. Concordance correlation coefficients (CCC) assessed agreement, and Cox regression and ROC analyses evaluated association with incident MACE.

Results: Automated scoring demonstrated high concordance with manual methods (CCC: 0.926-0.992), supporting its reliability in assessing aortic calcifications. ROC analysis revealed that the automated method was as effective as the manual technique in predicting MACE (p ​> ​0.05).

Conclusion: Automated aortic calcification scoring is a reliable alternative to manual methods, offering consistency and efficiency in the analysis of incidental findings on CAC scans.