Development and validation of a deep learning model for the automated detection of vertebral artery calcification on non-contrast head-and-neck computed tomography

Abstract Background Vertebral artery calcification (VAC), a critical indicator of cerebrovascular disease, is often overlooked in head-and-neck imaging. Manual detection is time-consuming and prone to inter-observer variability. This study aimed to develop and validate a deep learning model for automated detection and quantitative risk assessment of VAC in non-contrast head-and-neck computed tomography (CT) images, bridging the diagnostic gap between dentistry and vascular medicine. Methods We developed a deep learning model based on the ResNet-18 architecture, designated as Grayscale ResNet, optimized for single-channel CT images. The development followed a two-phase strategy: initial training on 539 axial images from head-and-neck CT image followed by iterative refinement (fine-tuning) using a targeted dataset of clinically significant cases to ensure generalizability. The model’s performance was evaluated using patient-level Receiver Operating Characteristic (ROC) analysis and saliency map visualization for clinical interpretability. Results The optimized model demonstrated a robust performance in distinguishing between cases with and without VAC. In the independent cohort, the model achieved an area under the curve (AUC) of 0.846. At a specific threshold value (98.6%), the system yielded a sensitivity of 80.0% and a specificity of 90.6%. A saliency map analysis confirmed that the model consistently focused on anatomically relevant vascular regions. Conclusions The proposed automated system provides an accurate and reliable method for VAC screening using routine head-and-neck CT scans. By transforming incidental imaging findings into a quantifiable risk index, this tool can serve as a vital decision-support system for dentists and radiologists, facilitating early patient referrals and contributing to global stroke prevention.