Detection of carotid artery calcifications using artificial intelligence in dental radiographs: a systematic review and meta-analysis.
Authors
Affiliations (10)
Affiliations (10)
- Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Hezar-Jarib Ave, Isfahan, 81551-39998, Iran. [email protected].
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.
- Maxillogram Maxillofacial Surgery, Implantology and Biomaterial Research Foundation, Istanbul, 8418829912, Turkey. [email protected].
- Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Hezar-Jarib Ave, Isfahan, 81551-39998, Iran.
- Scottish Craniofacial Research Group, School of Medicine, Dentistry and Nursing, Glasgow University MVLS College, Glasgow University Dental School, Glasgow, UK.
- Department of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al Ahsa, Saudi Arabia.
- Center for Artificial Intelligence and Innovation (CAII), Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand.
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Salerno, Italy.
- The Dental School, University of Bristol, Bristol, UK.
- The University of Jordan, Amman, Jordan.
Abstract
Carotid artery calcifications are important markers of cardiovascular health, often associated with atherosclerosis and a higher risk of stroke. Recent research shows that dental radiographs can help identify these calcifications, allowing for earlier detection of vascular diseases. Advances in artificial intelligence (AI) have improved the ability to detect carotid calcifications in dental images, making it a useful screening tool. This systematic review and meta-analysis aimed to evaluate how accurately AI methods can identify carotid calcifications in dental radiographs. A systematic search in databases including PubMed, Scopus, Embase, and Web of Science for studies on AI algorithms used to detect carotid calcifications in dental radiographs was conducted. Two independent reviewers collected data on study aims, imaging techniques, and statistical measures such as sensitivity and specificity. A meta-analysis using random effects was performed, and the risk of bias was evaluated with the QUADAS-2 tool. Nine studies were suitable for qualitative analysis, while five provided data for quantitative analysis. These studies assessed AI algorithms using cone beam computed tomography (n = 3) and panoramic radiographs (n = 6). The sensitivity of the included studies ranged from 0.67 to 0.98 and specificity varied between 0.85 and 0.99. The overall effect size, by considering only one AI method in each study, resulted in a sensitivity of 0.92 [95% CI 0.81 to 0.97] and a specificity of 0.96 [95% CI 0.92 to 0.97]. The high sensitivity and specificity indicate that AI methods could be effective screening tools, enhancing the early detection of stroke and related cardiovascular risks. Not applicable.