Kot Wai Ying, Au Yeung Sum Yin, Leung Yin Yan, Leung Pui Hang, Yang Wei-Fa
Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
Division of Oral & Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
BMC Oral Health. 2025 May 26;25(1):800. doi: 10.1186/s12903-025-05984-6.
Deep learning has been utilized to segment teeth from computed tomography (CT) or cone-beam CT (CBCT). However, the performance of deep learning is unknown due to multiple models and diverse evaluation metrics. This systematic review and meta-analysis aims to evaluate the evolution and performance of deep learning in tooth segmentation.
We systematically searched PubMed, Web of Science, Scopus, IEEE Xplore, arXiv.org, and ACM for studies investigating deep learning in human tooth segmentation from CT/CBCT. Included studies were assessed using the Quality Assessment of Diagnostic Accuracy Study (QUADAS-2) tool. Data were extracted for meta-analyses by random-effects models.
A total of 30 studies were included in the systematic review, and 28 of them were included for meta-analyses. Various deep learning algorithms were categorized according to the backbone network, encompassing single-stage convolutional models, convolutional models with U-Net architecture, Transformer models, convolutional models with attention mechanisms, and combinations of multiple models. Convolutional models with U-Net architecture were the most commonly used deep learning algorithms. The integration of attention mechanism within convolutional models has become a new topic. 29 evaluation metrics were identified, with Dice Similarity Coefficient (DSC) being the most popular. The pooled results were 0.93 [0.93, 0.93] for DSC, 0.86 [0.85, 0.87] for Intersection over Union (IoU), 0.22 [0.19, 0.24] for Average Symmetric Surface Distance (ASSD), 0.92 [0.90, 0.94] for sensitivity, 0.71 [0.26, 1.17] for 95% Hausdorff distance, and 0.96 [0.93, 0.98] for precision. No significant difference was observed in the segmentation of single-rooted or multi-rooted teeth. No obvious correlation between sample size and segmentation performance was observed.
Multiple deep learning algorithms have been successfully applied to tooth segmentation from CT/CBCT and their evolution has been well summarized and categorized according to their backbone structures. In future, studies are needed with standardized protocols and open labelled datasets.
深度学习已被用于从计算机断层扫描（CT）或锥束CT（CBCT）中分割牙齿。然而，由于模型多样且评估指标各异，深度学习的性能尚不清楚。本系统评价和荟萃分析旨在评估深度学习在牙齿分割中的发展及性能。
我们系统检索了PubMed、科学网、Scopus、IEEE Xplore、arXiv.org和ACM，以查找有关从CT/CBCT进行人类牙齿分割的深度学习研究。使用诊断准确性研究质量评估（QUADAS-2）工具对纳入的研究进行评估。通过随机效应模型提取数据进行荟萃分析。
系统评价共纳入30项研究，其中28项纳入荟萃分析。各种深度学习算法根据主干网络进行分类，包括单阶段卷积模型、具有U-Net架构的卷积模型、Transformer模型、具有注意力机制的卷积模型以及多种模型的组合。具有U-Net架构的卷积模型是最常用的深度学习算法。在卷积模型中集成注意力机制已成为一个新课题。确定了29种评估指标，其中Dice相似系数（DSC）最受欢迎。DSC的合并结果为0.93 [0.93, 0.93]，交并比（IoU）为0.86 [0.85, 0.87]，平均对称表面距离（ASSD）为0.22 [0.19, 0.24]，灵敏度为0.92 [0.90, 0.94]，95%豪斯多夫距离为0.71 [0.26, 1.17]，精度为0.96 [0.93, 0.98]。单根牙或多根牙分割中未观察到显著差异。样本量与分割性能之间未观察到明显相关性。
多种深度学习算法已成功应用于从CT/CBCT进行牙齿分割，并且根据其主干结构对它们的发展进行了很好的总结和分类。未来，需要采用标准化方案和开放标记数据集进行研究。
