Deep Learning Optimizes the Diagnostic Performance of Coronary Computed Tomography Angiography in Heavily Calcified Coronary Plaques: A Chinese Multicenter Study

Background: Accurate evaluation of coronary luminal stenosis using coronary computed tomography angiography (CCTA) remains significantly impaired in the presence of severe coronary artery calcification. Calcification-related blooming artifacts frequently lead to overestimation of stenosis severity and reduced diagnostic specificity, representing a persistent and unresolved challenge in clinical practice. Thus, this study aimed to develop and validate a self-supervised deep learning (DL) algorithm to improve the diagnostic performance of CCTA for assessing luminal stenosis in patients with heavily calcified coronary plaques (Agatston score >300), using quantitative coronary angiography (QCA) as the reference standard. Methods: This multicenter retrospective study enrolled 786 patients with agatston score (AS) >300 who underwent both CCTA and QCA from four Chinese hospitals. The dataset was randomly divided into a training group (80%, n = 628) for algorithm development and a validation group (20%, n = 158). The diagnostic performance of the DL model and radiologists was compared using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for detecting ≥50% diameter stenosis at the lesion, vessel, and patient levels. Reporting time was also evaluated. Results: In the validation cohort, 476 calcified lesions in 213 vessels from 93 patients were confirmed by QCA to have ≥50% diameter stenosis. The DL model demonstrated significantly higher specificity than radiologists at both the vessel level (52% vs. 30%) and patient level (74% vs. 57%) (all p < 0.05). In lesions with severe calcification (cross-sectional calcium arc 270°–360°), DL further improved lesion-level specificity and NPV by 27% and 36%, respectively (all p < 0.05). In addition, DL markedly reduced reporting time compared with radiologists (61.9 ± 2.56 s vs. 376.6 ± 127.2 s; p < 0.001). Conclusion: The proposed self-supervised DL model enables rapid and more accurate assessment of luminal stenosis in the presence of heavily calcified plaques, with improved diagnostic specificity and greater workflow efficiency.