The advancement of digital technologies in oral implantology has facilitated the progress of precise implant treatment, generating numerous quantitative indices relevant to clinical decision-making. In the transformation from digital implant to intelligent implant, accurate quantification of hard and soft tissues serves as the foundation for intelligent and precise implant therapy, and artificial intelligence (AI) has demonstrated potential applications in this field. However, the clinical translation of AI-based quantitative analysis in oral implantology remains constrained by challenges related to data acquisition, task specificity, algorithm design, and clinical validation. Drawing upon current evidence from domestic and international studies and our research findings in this area, this article elucidates the characteristics of quantitative tasks in oral implantology, summarizes the technical pathways for AI-based quantitative analysis, and discusses existing challenges and future directions, with the aim of providing a reference for the clinical development of intelligent quantitative analysis in oral implantology.

Root resorption represents a significant clinical challenge in orthodontic practice, necessitating the development of precise diagnostic techniques. This study employed an advanced Mask RCNN-Gate Context Aggregation (GCA) deep learning algorithm to analyze volumetric characteristics of maxillary structures, specifically focusing on canines and lateral incisors in patients with unilateral impacted canines. The study aimed to identify key morphological predictors for maxillary canine impaction and establish quantitative indicators for early clinical intervention.

CBCT data were obtained from 45 patients diagnosed with unilateral maxillary canine impaction. The dataset was partitioned into a training set (32 cases, 70%) , a validation set (9 cases, 20%) , and a test set (4 cases, 10%) . Following preprocessing, which included data augmentation techniques such as random rotation, affine shear, horizontal and vertical flipping, and image translation, a total of 4 500 two-dimensional images were generated to enhance the training of the Mask RCNN-GCA model. Model performance was assessed using the Dice similarity coefficient (DSC) , sensitivity, and accuracy. Additionally, volumetric differences in maxillary bone and dental structures were compared between the impacted and non-impacted sides with t-test analysis.

The refined model demonstrated outstanding segmentation performance, attaining a DSC of 93.60% and an accuracy of 96.50% in maxillary structures; for canine identification, the values were a DSC of 92.50% and an accuracy of 97.50%. Quantitative analysis revealed statistically significant volumetric reductions (t_{maxillary volume} = 2.90, P_{maxillary volume} = 0.01; t_{canine volume} = 2.42, P_{canine volume} = 0.005; t_{lateral incisor volume} = 3.11, P_{lateral incisor volume} = 0.006) on the impacted side for maxillary bone [mean difference: (8 992 ± 1 685) mm³], canine (8 767 mm³) , and lateral incisor (3 028 mm³) compared to contralateral measurements. Gender-based analysis showed no statistically relevant differences in any measured parameters (t_{impacted sides} = 0.77, P_{impacted sides} = 0.40; t_{non-impacted sides} = 0.23, P_{non-impacted sides} = 0.91) . No significant volumetric variation was observed in first premolars (t = 0.77, P = 0.402) .

The enhanced Mask RCNN-GCA framework provided an effective solution for automated, high-precision volumetric assessment of maxillary impaction cases. The identified volumetric disparities in maxillary bone and associated dentition offered clinically relevant morphological markers for early diagnosis and intervention. These findings contributed valuable quantitative references for minimizing orthodontic complications, particularly adjacent root resorption, through timely therapeutic strategies.

Smile aesthetic evaluation is a critical component of clinical treatment planning. As a key aesthetic indicator, the precise classification of the smile line is crucial for optimizing restorative and reconstructive treatment plans. However, classifying smile line requires accurate assessment of complex relationships among the lips, gingiva and teeth, and analysis by dentists involves a degree of subjectivity and chances for misdiagnosis. This study aimed to investigate smile line classification by comparing the performance of convolutional neural networks (CNNs) and large language models (LLMs) , as well as clinicians of varying expertise levels, in this task.

Based on the publicly available high-quality FFHQ facial dataset, a smile image annotation dataset comprising 1 000 samples was constructed following image preprocessing and standardized annotations of three types: high, medium and low smile line. Seven classic CNN models (VGG16, ResNet34, etc.) and five representative multimodal LLMs (Qwen series, LLaVA 1.5-7B) were employed for training, validation, and testing. Model performance was evaluated using accuracy, precision, recall, and F₁ scores, and compared against assessments made by clinicians of different seniority levels.

Among the seven commonly used CNN models, the ResNet152 model demonstrated optimal overall performance, achieving a mean classification accuracy of 83.30%, which significantly outperformed other CNN models and multimodal LLMs. Senior dentists achieved a classification accuracy of 83.00%, comparable to the performance of ResNet152. Heatmaps demonstrate similar attention regions between ResNet152 and dental practitioners.

CNN models demonstrated substantial clinical potential in smile line classification tasks, attaining expert-level performance. In contrast, large language models required further optimization for medical image fine-grained classification. This study provided experimental evidence and technical insights for developing intelligent aesthetic assessment systems in dentistry.

Artificial intelligence (AI) is profoundly transforming the paradigms of basic and clinical research in endodontics. In basic research, AI facilitates the in-depth exploration of the structural characteristics, biological mechanisms, and disease pathogenesis of the pulp-dentin complex through high-precision image segmentation, multimodal data integration, and bioinformatics analysis. In clinical research, AI optimizes the entire process of study design, disease diagnosis, and treatment outcome prediction, enhancing the scientific rigor, efficiency, and reproducibility of research. Despite challenges related to data quality, algorithm interpretability, and interdisciplinary integration, AI's potential as a "research collaborator" is increasingly evident, promising to advance endodontics into a new era of more precise, efficient, and innovative research.

Occlusal reconstruction serves as a critical approach for severe tooth wear and dentition defects. Recently, digital technology has increasingly integrated into the entire workflow of occlusal rehabilitation, covering data acquisition, virtual design and restoration fabrication, which has significantly enhanced both restoration precision and clinical efficiency. Guided by functional, aesthetic, and minimally invasive principles, this paper systematically reviews the clinical pathways and key techniques in occlusal rehabilitation. Furthermore, it explores the clinical value and current limitations of the digital workflow, aiming to provide clinical insights and research strategies for digital occlusal rehabilitation.

In recent years, artificial intelligence (AI) image processing technology has matured and been widely applied in stomatology, significantly advancing the field. This article systematically reviews its research and application progress, covering fundamental theories, key technologies, and clinical practices. Based on explanations of technologies including deep learning and machine learning, it focuses on analyzing specific applications across various dental images and discusses future directions, potential value, and associated ethical and legal challenges. To achieve more extensive and reliable clinical applications, key issues remain to be addressed.

Dental nursing is an integral component of clinical dental practice. With the increasing burden of oral diseases and the growing demand for dental healthcare services, traditional dental nursing models are facing substantial challenges. The rapid advancement of artificial intelligence (AI) technologies presents new opportunities for the transformation of dental nursing practice. This review summarizes recent research advances and current applications of AI in the field of dental nursing, with a particular focus on intelligent triage and referral, perioperative nursing collaboration, postoperative management and pain care, patient follow-up and health behavior management, as well as dental nursing education. Furthermore, this review discusses the transformation of dental nursing models in the AI era, aiming to promote the development of intelligent dental nursing systems.

To investigate the effects of different irradiation schemes (single-dose 20 Gy and fractionated 5 × 7 Gy) on a rat model of osteoradionecrosis of the jaws (ORNJ) , to optimize the modeling protocol.

Twenty-seven male Sprague-Dawley (SD) rats were randomly divided into a single-dose irradiation group (20 Gy) , a fractionated-dose irradiation group (5 × 7 Gy) , and a control group. Prior to irradiation, the subcutaneous depth and buccolingual diameter of the left mandible in rats were measured. A linear accelerator was used to precisely target the left mandibular molar region, supplemented with a compensator membrane to enhance superficial dose delivery. One week after irradiation, all left mandibular molars were extracted with the aid of a microscope. Twelve weeks post-extraction, gross observation, micro-CT scanning and reconstruction, as well as histopathological analyses (H & E, Masson's trichrome and TRAP staining) were performed to assess bone destruction, sequestrum formation, fibrosis, and osteoclast. Survival rate and modeling success rate were analyzed.

The single-dose group exhibited acute radiation reactions (skin redness, hair loss) and significant occlusal disorder after four weeks, with a modeling success rate of 71%. The fractionated -dose group showed milder acute reactions but more severe late-stage fibrosis, with a modeling success rate of 78%. Micro-CT revealed more obvious trabecular fractures, cortical bone disruption, and pathological fractures in the single-dose group, while the fractionated group exhibited deeper bone defects. Histologically, both experimental groups displayed necrotic bone formation, fibrous tissue hyperplasia, empty bone lacunae, increased fatty vacuoles, and osteoclast infiltration. The fractionated group demonstrated marked bone marrow fat vacuolization, fibrosis, and osteoclast infiltration.

The fractionated irradiation scheme (5 × 7 Gy) is more consistent with the pathological features of ORNJ. The optimized modeling method demonstrates high reproducibility and controllability, providing a reliable platform for investigating ORNJ mechanisms and developing prevention/therapeutic strategies.

This study aimed to evaluate the understanding and clinical implementation of the "aesthetics-function balance" concept among dentists in China when performing ceramic veneer restorations, and to provide evidence-based insights for optimizing clinical guidelines and training systems.

A cross-sectional, structured questionnaire survey was conducted nationwide through an anonymous online platform. The questionnaire covered key domains including indication assessment, tooth preparation, material selection, bonding and isolation protocols. Descriptive statistics were used to summarize overall trends, and chi-square tests were applied to compare differences in key clinical behaviors across groups stratified by institution type, professional title, and annual veneer case volume.

A total of 224 valid responses were collected from 31 provinces. Most respondents demonstrated a clear awareness of functional risks: 214 (95.54%) identified "indication selection" as the primary determinant of veneer success, and 216 (96.43%) expressed strong concern regarding occlusal abnormalities and bruxism. Lithium disilicate ceramic was the most commonly selected material 173 (77.23%) . Treatment design favored a balance between minimal invasiveness and functional stability: 195 (87.05%) preferred window-type or butt-joint preparations for color improvement cases, while 179 (79.91%) selected wrap-around preparations when incisal or palatal defects were present to enhance fracture resistance. Although 180 (80.36%) of dentists had read veneer-related guidelines or expert consensus documents, only 80 (35.71%) reported frequent consultations in clinical practice; routine rubber dam use was also low (35.27%) . Subgroup analyses revealed significant differences across institution types, professional titles, and case experience levels in terms of material knowledge, preparation design choices, and guideline adherence (P<0.05) .

While most dentists have established the core concept of balancing aesthetics and function, notable variations persist in material knowledge, preparation strategies, and the practical implementation of clinical guidelines. A gap remains between conceptual understanding and clinical execution, highlighting the need for enhanced training and standardized clinical pathways to improve the predictability and quality of ceramic veneer restorations.

Pyostomatitis vegetans (PSV) is an uncommon inflammatory disorder of the oral mucosa, frequently associated with inflammatory bowel disease. IgA pemphigus, a rare autoimmune bullous disease, seldom involves mucosal tissues. We present a case of IgA pemphigus initially manifesting as PSV, with subsequent development of pustular lesions on the posterior trunk, neck, back, and perianal region, accompanied by gastrointestinal symptoms and peer reactions. The patient's condition improved following combination therapy with glucocorticoids, thalidomide, and rituximab. The extensive mucosal involvement observed in this case broadens the understanding of the clinical spectrum of IgA pemphigus. This report highlights that PSV may serve as an initial manifestation of IgA pemphigus, which should therefore be considered in the differential diagnosis of such oral lesions. Multidisciplinary management is essential for accurate diagnosis and effective treatment.

Root canal irrigation plays a vital role in root canal therapy. Due to the complexity of the root canal system, mechanical preparation alone cannot eradicate infections, so effective root canal irrigation is the critical factor for the success of root canal therapy. In clinical practice, a combination of irrigants is commonly employed to remove residual pulp tissue, the smear layer and residual microorganisms in the root canal system, so as to improve the cleaning effect of root canal irrigation. This article reviews the interaction and common problems between combined irrigants, and puts forward suggestions for the combined use strategy of root canal irrigants.