Machine-Learning Based Detection of Coronary Artery Calcification Using Synthetic Chest X-Rays

• URL: http://arxiv.org/abs/2511.11093v1
• Date: Fri, 14 Nov 2025 09:11:41 GMT
• Title: Machine-Learning Based Detection of Coronary Artery Calcification Using Synthetic Chest X-Rays
• Authors: Dylan Saeed, Ramtin Gharleghi, Susann Bier, Sonit Singh,
• Abstract summary: Coronary artery calcification is a strong predictor of cardiovascular events.<n>Digitally reconstructed radiographs (DRRs) offer a scalable alternative by projecting CT volumes into CXR-like images.<n>We provide the first systematic evaluation of DRRs as a surrogate training domain for CAC detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coronary artery calcification (CAC) is a strong predictor of cardiovascular events, with CT-based Agatston scoring widely regarded as the clinical gold standard. However, CT is costly and impractical for large-scale screening, while chest X-rays (CXRs) are inexpensive but lack reliable ground truth labels, constraining deep learning development. Digitally reconstructed radiographs (DRRs) offer a scalable alternative by projecting CT volumes into CXR-like images while inheriting precise labels. In this work, we provide the first systematic evaluation of DRRs as a surrogate training domain for CAC detection. Using 667 CT scans from the COCA dataset, we generate synthetic DRRs and assess model capacity, super-resolution fidelity enhancement, preprocessing, and training strategies. Lightweight CNNs trained from scratch outperform large pretrained networks; pairing super-resolution with contrast enhancement yields significant gains; and curriculum learning stabilises training under weak supervision. Our best configuration achieves a mean AUC of 0.754, comparable to or exceeding prior CXR-based studies. These results establish DRRs as a scalable, label-rich foundation for CAC detection, while laying the foundation for future transfer learning and domain adaptation to real CXRs.

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