Fan Wenjie, Zhang Jiaqi, Wang Nan, Li Jia, Hu Li
Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Diagnostics (Basel). 2023 Jun 14;13(12):2056. doi: 10.3390/diagnostics13122056.
Cone beam computed tomography (CBCT) has become an essential tool in modern dentistry, allowing dentists to analyze the relationship between teeth and the surrounding tissues. However, traditional manual analysis can be time-consuming and its accuracy depends on the user's proficiency. To address these limitations, deep learning (DL) systems have been integrated into CBCT analysis to improve accuracy and efficiency. Numerous DL models have been developed for tasks such as automatic diagnosis, segmentation, classification of teeth, inferior alveolar nerve, bone, airway, and preoperative planning. All research articles summarized were from Pubmed, IEEE, Google Scholar, and Web of Science up to December 2022. Many studies have demonstrated that the application of deep learning technology in CBCT examination in dentistry has achieved significant progress, and its accuracy in radiology image analysis has reached the level of clinicians. However, in some fields, its accuracy still needs to be improved. Furthermore, ethical issues and CBCT device differences may prohibit its extensive use. DL models have the potential to be used clinically as medical decision-making aids. The combination of DL and CBCT can highly reduce the workload of image reading. This review provides an up-to-date overview of the current applications of DL on CBCT images in dentistry, highlighting its potential and suggesting directions for future research.
锥形束计算机断层扫描（CBCT）已成为现代牙科的一项重要工具，使牙医能够分析牙齿与周围组织之间的关系。然而，传统的手动分析可能耗时，其准确性取决于用户的熟练程度。为了解决这些局限性，深度学习（DL）系统已被集成到CBCT分析中，以提高准确性和效率。已经开发了许多DL模型用于诸如自动诊断、分割、牙齿、下牙槽神经、骨骼、气道分类以及术前规划等任务。汇总的所有研究文章均来自截至2022年12月的PubMed、IEEE、谷歌学术和科学网。许多研究表明，深度学习技术在牙科CBCT检查中的应用取得了显著进展，其在放射学图像分析中的准确性已达到临床医生水平。然而，在某些领域，其准确性仍需提高。此外，伦理问题和CBCT设备差异可能会阻碍其广泛应用。DL模型有潜力作为医疗决策辅助工具用于临床。DL与CBCT的结合可以大大减少图像解读的工作量。本综述提供了DL在牙科CBCT图像上当前应用的最新概述，突出了其潜力并为未来研究提出了方向。
