Introduction: There is still a need for understanding its mechanism about association between oral hypofunction and physical activity. We investigated this association by assessing oral function comprehensively in longitudinal study, specifically focusing on the roles of muscle strength and cognitive function. Materials and Methods: Community-dwelling elderly data from the Korean Social Life, Health, and Aging Project cohort during 2016 to 2019 was used. Finally, 454 participants were included. Oral hypofunctions such as the number of teeth, mastication, swallowing, and salivation discomfort were used as exposure. Activities of Daily Living (ADL) including instrumental ADL and handgrip strength and Mini-Mental State Examination (MMSE) were used as outcome and mediation-suspecting variables, respectively. To examine the association between oral hypofunction and physical life activities considering repeated measurements, Generalized Estimating Equations regression model was used after adjusting covariates. Also, Baron and Kenny's mediation method was applied to test handgrip strength and MMSE as mediators. Results: The results revealed that subjective discomfort in mastication or swallowing was associated with difficulties in ADL or IADL (P<0.05). Specifically, having more than two discomfort in mastication, swallowing, or salivation were significantly associated with poor ADL (P<0.001). However, the number of teeth was not associated with ADL, including instrumental ADL. Particularly among those having more than two discomfort oral functions, muscle strength was shown to fully mediate this association while MMSE was found to partially mediate (P<0.001). Conclusion: These findings suggest having subjective discomfort in important oral functions might be related to deteriorated physical activities among older adults. Therefore, careful attention might have to be paid to the deterioration of the subjective oral function of the elderly in terms of physical independence.

Purpose: This study aimed to assess age-related differences in oral motor function using both objective and subjective measures and to identify factors associated with masticatory performance (MP), oral diadochokinesis (ODK), and tongue pressure (TP) in older adults. Materials and Methods: A total of 227 healthy older adults aged ≥65 years participated in this study and were classified into Group 1 (Gr1; 65 - 74 years, n=109) and Group 2 (Gr2; ≥75 years, n=118). The objective assessments included MP, ODK, TP, bite force (BF), occluding area (OA), and the Eichner index. The subjective assessments included the Food Intake Ability (FIA) questionnaire, Oral Health Impact Profile (OHIP)-14, and Korean Mini-Mental State Examination (KMMSE). Multiple linear regression analysis was performed to examine the variables associated with MP, ODK and TP. Results: ODK, TP and KMMSE scores significantly decreased in the ≥75-year age group, whereas MP did not significantly differ between the groups. In Gr1, MP was associated with OA, ODK (/pa/, /ta/), BF, and FIA. In Gr2, MP was associated with ODK (/pa/,/ta/,/ka/) and BF. In Gr2, TP was associated with OA, ODK, BF, and the KMMSE. ODK was additionally associated with the OHIP-14 and KMMSE only in Gr2. Conclusion: Although both groups maintained sufficient masticatory function, older adults aged ≥75 years showed marked declines in oral motor and cognitive function. These findings emphasize the importance of age-specific assessment and early intervention to maintain oral health and functional independence in older adults.

Purpose: Precise registration between intraoral scans (IOSs) and CBCT images is essential for many digital dentistry applications. The purpose of this study was to develop DentalRegiformer, a fully automated, end-to-end deep learning framework that ensures accurate registration between IOS and CBCT images. Materials and Methods: The network was trained on 88 paired CBCT-IOS datasets from 44 patients, with ground-truth alignments obtained through manual registration performed by trained dental students. To enhance clinical accuracy and efficiency, a multi-stage coarse-to-fine strategy is proposed to integrate segmentation, classification, and registration into a unified pipeline. First, tooth segmentation is performed on CBCT volumes using 3D nnU-Net to generate segmented tooth regions for alignment. Simultaneously, IOS datasets are classified as maxillary or mandibular dentition using a hybrid representation that combines geometric descriptors with point cloud features extracted via a DGCNN network. Registration is then conducted in two phases: coarse alignment using a DCP framework for feature encoding and probabilistic correspondence estimation to compute the initial rigid transformation, followed by fine alignment using GeoTransformer's local-to-global iterative refinement, which alternates between local feature aggregation and global context integration to progressively enhance correspondence accuracy and achieve precise rigid registration. Results: The segmentation model achieved robust results, with a Dice coefficient of 0.924, precision of 0.916, and recall of 0.934. The DGCNN classifier perfectly distinguished maxillary and mandibular arches, achieving sensitivity, specificity, ROC, and accuracy of 1.000. For registration, DentalRegiformer accurately predicted transformation between IOS and CBCT images, yielding an overall RMSE of 0.556 mm; MAEs of 0.231mm, 0.329 mm, and 0.283 mm along the x-, y-, and z-axes, respectively; and inference time of 19.623 seconds. In comparison, the manual method based on combined pair-points and ICP had an RMSE of 0.615 mm; MAEs of 0.244 mm, 0.347 mm, and 0.298 mm along the x-, y-, and z-axes, respectively; and processing time of 431.6 seconds. Discussion and Conclusion: Therefore, DentalRegiformer is a fully automated, deep learning-based solution that minimizes manual intervention and improves clinical efficiency. The framework streamlines digital dental workflows, reduces operator-dependent errors, and facilitates automated, personalized dental care.

Purpose: This study aimed to develop and evaluate a multimodal deep learning model that integrates panoramic radiographs and clinical data to improve the diagnosis of medication-related osteonecrosis of the jaw (MRONJ). Materials and Methods: The study included 705 panoramic radiographs (401 MRONJ and 304 Normal) collected from Ewha Womans University Mokdong and Seoul Hospitals. Radiographs were paired with patient information, including age, sex, body mass index (BMI), type of medication, duration, route of administration, dosage, and dental risk factors. Two deep learning models were compared: a ResNet-50 model using panoramic radiographs alone and the Modified ResNet-50 model incorporating both image and clinical features. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Lesion localization and feature importance were analyzed using Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP). Result: Significant differences in clinical characteristics were observed between the groups; the MRONJ group was older (74.5±10.0 vs. 68.6±12.7 years, P<0.01) and had a higher proportion of females (92.5% vs. 82.2%, P<0.01). In model performance, the Modified ResNet-50 model outperformed the PR-only model. Specifically, the Modified ResNet-50 model achieved an accuracy of 75.0%, a recall of 84.3%, and an AUC of 0.843, whereas the PR-only model showed lower performance with an accuracy of 72.1%, a recall of 81.4%, and an AUC of 0.720. SHAP analysis identified prolonged medication use and specific drug types (e.g., zoledronate, P<0.01) as influential predictors. Conclusion: The proposed multimodal deep learning framework demonstrated improved diagnostic performance and interpretability compared to radiograph-only models. This approach may serve as a practical and explainable AI-assisted tool for MRONJ diagnosis in routine dental practice.

Objectives: Lateral luxation is a traumatic dental injury generally managed through prompt repositioning and splinting, which is essential for pulp and periodontal healing. But delays in treatment and anatomical constraints can complicate treatment. This report aims to describe the clinical course and prognosis of a case in which maxillary central incisors were inadvertently splinted in an incorrect position outside the socket and subsequently managed with regenerative endodontic treatment. Methods: A 7-year-old girl presented with laterally luxated maxillary central incisors that had been improperly splinted out of the socket at another local dental clinic. The malpositioning was identified 4 weeks post-trauma via cone-beam computed tomography, at which point repositioning was deemed difficult, and the immature teeth were under observation. Subsequently, regenerative endodontic treatment was performed because of pulpal necrosis and signs of inflammation. Results: At 31 months of follow-up after the injury, favorable healing outcomes were observed, including formation of the lamina dura, reduction in apical foramen diameter, increased clinical crown length, and the absence of clinical symptoms. Conclusion: This case demonstrates that, even when immature teeth remain incorrectly positioned after trauma and present with a lack of alveolar support and direct gingival exposure of the root apex, favorable outcomes can still be achieved following pulp therapy.

This report presents a case of complete-arch rehabilitation using the Hobo twin-stage technique in a patient with unstable mandibular movement and posterior overclosure, defined as a loss of vertical dimension resulting from the loss or drifting of posterior teeth. Standardized cusp angles were applied to establish predictable posterior disocclusion, eliminating the need for patient-specific condylar recordings. A fully digital workflow, incorporating virtual articulators and computer-aided design software, facilitated the streamlined design and fabrication of prosthesis. The integration of digital technology reduced laboratory workload and enhanced reproducibility. This approach offers an efficient and reliable solution for complex fixed prosthetic treatments.

Following tooth extraction, inevitable hard and soft tissue remodeling leads to buccal contour loss, and complete prevention remains challenging despite strategies including ridge preservation, immediate implant placement and bone grafting. To rehabilitate the contour loss, soft tissue augmentation with a collagen matrix is widely performed to increase crestal mucosal thickness. In addition, the use of customized healing abutment (HA) contributes to establishing harmonious buccal transmucosal margin with an adjacent natural tooth. This case report presents two cases in which ridge contour loss following extraction and implant placement that were reconstructed to the pre-extraction contour synergistically via soft tissue augmentation and customized HA. Horizontal soft tissue thickness changes were quantitatively assessed by superimposing stereolithographic files obtained from sequential intraoral scans, and the rehabilitation rate was calculated. At 3 months postoperatively, the rehabilitation rate at the 5-mm measurement point was 52.9% in Case 1 treated with a bilayer collagen matrix, and 102.9% in Case 2 treated with a volume-stable collagen matrix for soft tissue augmentation. With combined use of customized HA, the present cases established harmonious peri-implant mucosal architecture and emergence profiles of implant restoration.