SynthesisInternational dental journal2026
Artificial Intelligence-assisted Diagnosis of Carotid Artery Calcifications on Panoramic Radiographs: A Meta-analysis.
Synthesis in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Carotid artery calcifications (CACs), a known risk factor for stroke, can be detected on panoramic radiographs (PRs). However, this clinically significant pathophysiological condition has long been underdiagnosed due to insufficient training and expertise among dentists. Artificial intelligence (AI) may serve as a valuable tool to aid dentists in detecting CACs on PRs. This meta-analysis was conducted to assess the diagnostic accuracy of AI for CACs detection on PRs. A literature search was conducted on PubMed, Embase, Web of Science, Scopus and Cochrane Library up to 4 September 2025 without language limitation. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. The performance of AI was assessed via the area under curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Meta-analysis was conducted on Stata 14.0. This systematic review identified 8 relevant articles, 7 of which were eligible for meta-analysis. The analysis was conducted from two perspectives: per-side (evaluating the left and right sides of participants separately) and per-person. In the per-side analysis, the summary estimates indicated high diagnostic accuracy: sensitivity was 0.88 (95% CI: 0.84-0.92), specificity was 0.94 (95% CI: 0.91-0.97), the PLR was 15.8 (95% CI: 9.0-27.6), the NLR was 0.12 (95% CI: 0.08-0.18), the DOR was 129 (95% CI: 55-305), and AUC was 0.96. The per-person analysis yielded a pooled sensitivity of 0.90 (95% CI: 0.74-0.97) and specificity of 0.81 (95% CI: 0.73-0.87). The corresponding PLR was 4.6 (95% CI: 3.1-7.0), NLR was 0.12 (95% CI: 0.04-0.36), DOR was 37 (95% CI: 10-144), and the AUC was 0.86. These results indicate that AI may serve as a valuable tool to assist dentists in detecting CACs on PRs. However, large-scale, evidence-based studies are still needed to validate these findings.
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