Arzani Sarah, Soltani Parisa, Karimi Ali, Yazdi Maryam, Ayoub Ashraf, Khurshid Zohaib, Galderisi Domenico, Devlin Hugh
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.
Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.
BMC Med Imaging. 2025 May 19;25(1):174. doi: 10.1186/s12880-025-01719-9.
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.
颈动脉钙化是心血管健康的重要标志物，常与动脉粥样硬化及更高的中风风险相关。近期研究表明，牙科X光片有助于识别这些钙化，从而实现对血管疾病的早期检测。人工智能（AI）的进展提高了在牙科影像中检测颈动脉钙化的能力，使其成为一种有用的筛查工具。本系统评价和荟萃分析旨在评估AI方法在牙科X光片中识别颈动脉钙化的准确性。
在包括PubMed、Scopus、Embase和Web of Science在内的数据库中进行系统检索，以查找用于检测牙科X光片中颈动脉钙化的AI算法研究。两名独立评审员收集了关于研究目的、成像技术以及敏感性和特异性等统计指标的数据。进行了随机效应荟萃分析，并使用QUADAS-2工具评估偏倚风险。
九项研究适合进行定性分析，五项研究提供了定量分析数据。这些研究使用锥束计算机断层扫描（n = 3）和全景X光片（n = 6）评估了AI算法。纳入研究的敏感性范围为0.67至0.98，特异性在0.85至0.99之间。通过在每项研究中仅考虑一种AI方法，总体效应大小为敏感性0.92 [95%置信区间0.81至0.97]，特异性0.96 [95%置信区间0.92至0.97]。
高敏感性和特异性表明AI方法可能是有效的筛查工具，可加强对中风及相关心血管风险的早期检测。
不适用。
