Khanagar Sanjeev B, Albalawi Farraj, Alshehri Aram, Awawdeh Mohammed, Iyer Kiran, Alsomaie Barrak, Aldhebaib Ali, Singh Oinam Gokulchandra, Alfadley Abdulmohsen
Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia.
King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia.
Diagnostics (Basel). 2024 May 22;14(11):1079. doi: 10.3390/diagnostics14111079.
Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency in their approach, the researchers followed the diagnostic test accuracy guidelines outlined in PRISMA-DTA for this systematic review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library to identify relevant articles published between the years 2000 and 2024. A total of 26 articles that satisfied the inclusion criteria were subjected to a risk of bias assessment using QUADAS-2, which revealed a flawless risk of bias in both arms for the patient-selection domain. Additionally, the certainty of evidence was evaluated using the GRADE approach. AI technology has primarily been utilized for automated age estimation through tooth development stages, tooth and bone parameters, bone age measurements, and pulp-tooth ratio. The AI models employed in the studies achieved a remarkably high precision of 99.05% and accuracy of 99.98% in the age estimation for models using tooth development stages and bone age measurements, respectively. The application of AI as an additional diagnostic tool within the realm of age estimation demonstrates significant promise.
由于其潜在的实际用途，自动年龄估计已引起研究人员的极大兴趣。本次系统综述旨在严格评估使用牙颌面放射图像进行自动估计的人工智能模型的发展情况和性能。为了确保方法的一致性，研究人员在本次系统综述中遵循了PRISMA-DTA中概述的诊断试验准确性指南。他们在多个数据库中进行了电子检索，如PubMed、Scopus、Embase、Cochrane、Web of Science、谷歌学术和沙特数字图书馆，以识别2000年至2024年期间发表的相关文章。共有26篇符合纳入标准的文章使用QUADAS-2进行了偏倚风险评估，结果显示在患者选择领域的两个方面均无偏倚风险。此外，使用GRADE方法评估了证据的确定性。人工智能技术主要通过牙齿发育阶段、牙齿和骨骼参数、骨龄测量以及牙髓与牙齿比例来进行自动年龄估计。在使用牙齿发育阶段和骨龄测量的模型中，研究中使用的人工智能模型在年龄估计方面分别达到了99.05%的高精度和99.98%的高准确率。人工智能作为年龄估计领域的一种辅助诊断工具具有巨大的应用前景。
