[Effect of maxillary expansion combined with orofacial myofunctional therapy on the position of the tongue of children with mouth breathing].

Objective:This study aimed to investigate the change of the position of the tongue before and after combined treatment of maxillary expansion and orofacial myofunctional therapy in children with mouth-breathing and skeletal class Ⅱmalocclusion. Methods:A total of 30 children with skeletal class Ⅱ malocclusion and unobstructed upper airway were selected. The 30 children were divided into mouth-breathing group（n=15） and nasal-breathing group（n=15） and CBCT was taken. The images were measured by Invivo5 software. The measurement results of the tongue position of the two groups were analyzed by independent samples t-test. 15 mouth-breathing children with skeletal class Ⅱ malocclusion were selected for maxillary expansion and orofacial myofunctional therapy. CBCT was taken before and after treatment, the measurements were analyzed by paired sample t test with SPSS 27.0 software package. Results:The measurement of the tongue position of the mouth-breathing and nasal-breathing groups were compared, the differences were statistically significant（P<0.05）. The measurement of the tongue position showed significant difference after the combined treatment of maxillary expansion and orofacial myofunctional therapy in children with mouth-breathing and skeletal class Ⅱmalocclusion（P<0.05）. Conclusion:Skeletal class Ⅱ malocclusion children with mouth-breathing have low tongue posture. The combined treatment of maxillary expansion and orofacial myofunctional therapy can change the position of the tongue.

The Construction and Evaluation of a Multi-Task Convolutional Neural Network for a Cone-Beam Computed-Tomography-Based Assessment of Implant Stability.

Objectives: 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). Methods: 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. Results: 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. Conclusions: 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.