ROLE OF AI IN PREDICTING CARDIOVASCULAR RISK USING ROUTINE DENTAL IMAGING: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) remains the leading global cause of mortality, often progressing asymptomatically until advanced stages. Routine dental imaging, particularly panoramic radiographs, may incidentally capture vascular calcifications indicative of subclinical atherosclerosis. While artificial intelligence (AI) has shown potential in automating such detections, current evidence is fragmented, and no prior systematic review has synthesized its diagnostic value in this context. Objective: This systematic review aimed to evaluate the accuracy and clinical utility of AI algorithms in identifying early cardiovascular risk indicators from routine dental radiographs in adult populations. Methods: Following PRISMA guidelines, a systematic review was conducted using PubMed, Scopus, Web of Science, and the Cochrane Library to identify relevant studies published between 2019 and 2024. Eligible studies included cross-sectional and retrospective designs using AI models to detect cardiovascular risk markers via dental imaging. Data were extracted on study characteristics, AI model performance, and risk of bias, which was assessed using the Newcastle-Ottawa Scale. A narrative synthesis was conducted due to heterogeneity in model types and outcome measures. Results: Eight studies comprising 788 to 3,200 participants were included. All employed deep learning-based AI algorithms, primarily convolutional neural networks, to detect markers such as carotid artery calcifications. Reported accuracies ranged from 82.5% to 95.3%, with AUC values up to 0.91. Most studies demonstrated moderate-to-high methodological quality. However, variability in model training and limited external validation restricted meta-analytic pooling. Conclusion: AI algorithms show strong potential in identifying early cardiovascular risk using dental radiographs, offering a novel, non-invasive screening opportunity within dental care. Despite promising results, further prospective studies with standardized methodologies and external validation are essential to support clinical integration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".