Accuracy of deep learning in the detection of carotid calcifications on cone-beam computed tomography: A systematic review.

Purpose: This systematic review aimed to describe the diagnostic performance of AI-based models in identifying carotid calcifications using cone-beam computed tomography (CBCT) images. Materials and Methods: A comprehensive search was conducted in the PubMed/MEDLINE, Embase, IEEE Xplore, SciELO, and LILACS databases to identify relevant studies published up to 2025 that evaluated the diagnostic accuracy of artificial intelligence or deep learning systems in detecting carotid artery calcifications using CBCT. Grey literature sources were systematically searched. A qualitative synthesis was conducted for the included studies, followed by a diagnostic accuracy meta-analysis using sensitivity and specificity data. Analyses were performed using bivariate random-effects models (Diagnostic Random-Effects Model). Heterogeneity among studies was assessed using Cochran's Q test, the I Results: A total of 529 records were identified. No additional studies were retrieved from the grey literature. Application of inclusion and exclusion criteria resulted in the selection of four studies for qualitative synthesis. Conclusion: Despite variations in convolutional neural network (CNN) model architectures, all studies demonstrated that deep learning algorithms applied to CBCT achieve high performance levels in detecting carotid calcifications. The meta-analysis demonstrated that CNN-based models have high diagnostic potential for detecting carotid artery calcifications on CBCT. Although CBCT does not replace gold-standard diagnostic modalities, its use may represent a supportive tool for early screening and clinical referral. The expansion of datasets and the standardization of image acquisition protocols and quality are recommended.