Huang Zelun, Zheng Haoran, Huang Junqiang, Yang Yang, Wu Yupeng, Ge Linhu, Wang Liping
Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangzhou 510182, China.
Department of Chemical & Materials Engineering, University of Auckland, Auckland 1010, New Zealand.
Diagnostics (Basel). 2022 Nov 3;12(11):2673. doi: 10.3390/diagnostics12112673.
Assessing implant stability is integral to dental implant therapy. This study aimed to construct a multi-task cascade convolution neural network to evaluate implant stability using cone-beam computed tomography (CBCT). A dataset of 779 implant coronal section images was obtained from CBCT scans, and matching clinical information was used for the training and test datasets. We developed a multi-task cascade network based on CBCT to assess implant stability. We used the MobilenetV2-DeeplabV3+ semantic segmentation network, combined with an image processing algorithm in conjunction with prior knowledge, to generate the volume of interest (VOI) that was eventually used for the ResNet-50 classification of implant stability. The performance of the multitask cascade network was evaluated in a test set by comparing the implant stability quotient (ISQ), measured using an Osstell device. The cascade network established in this study showed good prediction performance for implant stability classification. The binary, ternary, and quaternary ISQ classification test set accuracies were 96.13%, 95.33%, and 92.90%, with mean precisions of 96.20%, 95.33%, and 93.71%, respectively. In addition, this cascade network evaluated each implant's stability in only 3.76 s, indicating high efficiency. To our knowledge, this is the first study to present a CBCT-based deep learning approach CBCT to assess implant stability. The multi-task cascade network accomplishes a series of tasks related to implant denture segmentation, VOI extraction, and implant stability classification, and has good concordance with the ISQ.
评估种植体稳定性是牙种植治疗不可或缺的一部分。本研究旨在构建一个多任务级联卷积神经网络，以使用锥形束计算机断层扫描（CBCT）来评估种植体稳定性。从CBCT扫描中获得了779张种植体冠部截面图像的数据集，并将匹配的临床信息用于训练和测试数据集。我们开发了一种基于CBCT的多任务级联网络来评估种植体稳定性。我们使用MobileNetV2-DeepLabV3+语义分割网络，结合图像处理算法并结合先验知识，生成最终用于种植体稳定性ResNet-50分类的感兴趣体积（VOI）。通过比较使用Osstell设备测量的种植体稳定性商（ISQ），在测试集中评估了多任务级联网络的性能。本研究中建立的级联网络对种植体稳定性分类显示出良好的预测性能。二元、三元和四元ISQ分类测试集的准确率分别为96.13%、95.33%和92.90%，平均精确率分别为96.20%、95.33%和93.71%。此外，该级联网络仅需3.76秒即可评估每个种植体的稳定性，表明效率很高。据我们所知，这是第一项提出基于CBCT的深度学习方法来评估种植体稳定性的研究。该多任务级联网络完成了一系列与种植义齿分割、VOI提取和种植体稳定性分类相关的任务，并且与ISQ具有良好的一致性。
