Deep learning-based assessment of coronary artery disease using curved-multiplanar reconstruction: comprehensive evaluation of stenosis, calcification, and plaque.

Introduction: This study developed and validated a deep learning-based framework to detect and quantify coronary artery stenosis, vulnerable plaque, and calcification in Curved-Multiplanar Reconstruction (cMPR) images to support clinical decision-making in coronary artery disease (CAD). Methods: We analyzed 1,715 patients (5,112 vessels) from 2014 to 2022. Each vessel was reconstructed into a cMPR scan consisting of 13 sequential cross-sectional slices. Using a 2D nnU-Net framework, we developed a Stenosis Segmentation Model (SSM) and a Vulnerable Plaque Segmentation Model (VPSM). A time-independent test set ( Results: The SSM achieved a vessel-level sensitivity of 0.84 and a high NPV of 0.98. The MAE for diameter stenosis was 12.4%, with a mean bias of +1.2% in Bland-Altman analysis, demonstrating robust agreement with expert references across the full test spectrum. For vulnerable plaque detection, the VPSM achieved a sensitivity of 0.80 and an NPV of 0.97 at the slice level. Calcification assessment showed high inter-rater reliability (ICC = 0.84) and substantial agreement with expert visual scoring (Kappa = 0.76). Conclusion: The proposed automated analysis demonstrated high diagnostic reliability, particularly in its negative predictive power, making it a powerful non-invasive tool for CAD screening. By providing objective quantification of stenosis, calcification, and vulnerable plaques, this method offers a significant advancement in standardized cMPR evaluation in clinical environments.