中华口腔医学杂志最新文献

Peri-implant disease is an oral disease that occurs with dental implant treatment. It is an inflammatory disease of peri-implant tissues caused by plaque biofilm, which can be divided into peri-implant mucositis and peri-implantitis. Peri-implant mucositis is limited in the soft tissues surrounding the implant and can be relieved through effective treatment. However if untreated, the inflammation can affect the bone around the implant and develop into peri-implantitis. Progressive resorption of supporting bone can ultimately lead to loss of osseointegration, which is the main cause of implant failure. There is a high incidence of peri-implant disease worldwide, which has become an increasingly serious public health problem. Prevention and supportive treatment of peri-implant disease is an important aspect of promoting oral health and deserves the attention of all dental professionals. This article combines literature review and relevant researches conducted by our research group in the past 15 years to introduce the control of risk factors, prevention, and supportive treatment of peri-implant disease. The aim is to raise the great attention of dentists, who engaged in oral implantation and restoration, to peri-implant and periodontal health, and master the prevention and supportive treatment methods of peri-implant disease.

Vertical root fracture (VRF) often occurs in endodontically treated teeth. Root canal therapy (RCT) requires removal of part of the dentin, which affects the microhardness and elastic modulus of dentine, induces dentin microcracks, and increases the risk of VRF. The early diagnosis of VRF is challenging and the prognosis is poor, due to the absence of specific clinical manifestations. This narrative review analyzes the incidence and related factors of VRF after RCT from the perspective of etiology, discusses the effect and mechanism of each step during RCT on the occurrence of VRF, and briefly summarizes the diagnosis, prevention and treatment strategies of VRF, so as to reduce the occurrence of VRF and improve the prognosis of endodontically treated teeth.

The use of antimicrobial agents is an important measure for the treatment of oral and maxillofacial space infection(OMSI). The irrational use of antimicrobials will not only affect the effect of disease treatment, but also leads to the occurrence of bacterial resistance. To standardize the rational use of antimicrobial agents in the treatment of OMSI, this consensus was developed based on the latest evidence-based medical research, incorporating extensive input from pharmaceutical and oral clinical experts, and refined through multiple rounds of discussion and revision. This consensus mainly reviews the anti-infective treatment regimen, common drug use methods, pharmaceutical monitoring, and treatment duration for OMSI. It aims to provide guidance for oral clinicians in the rational use of antimicrobial agents during the treatment of such infections.

With the continuous advancement of dental medicine and the increasing prevalence of digital technology, occlusal reconstruction has entered a new era characterized by high precision, efficiency, and predictability. This paper focuses on the digital workflow of full-dentition occlusal reconstruction, integrating multimodal data such as intraoral scanning, facial scanning, cone-beam CT, and mandibular movement tracking. By utilizing a domestic artificial intelligence platform, a virtual patient model for oral rehabilitation is established. The workflow encompasses five key stages: preliminary preparation, digital adjustment of jaw relations, transitional restoration verification, definitive restoration, and follow-up maintenance, ensuring the precise realization of treatment objectives. The core advantages of digital technology lie in the efficient integration of multimodal data, visualized treatment planning, and accurate clinical execution. In view of technical limitations, this paper emphasizes data quality control and related challenges. This digital pathway not only significantly enhances the efficacy of occlusal reconstruction, but also optimizes the patient experience, representing an important future direction for restorative dentistry.

The integration of digital technology with dentistry has become a central driving force in 21st-century dental innovation. Within the realm of occlusal function research, analytical methodologies are undergoing a paradigm shift: evolving from traditional empirical models toward multidimensional intelligent systems capable of digital precision assessment. The critical need for accurate occlusal contact analysis in both clinical practice and scientific research underscores the strategic importance of selecting context-appropriate analysis technologies, a process fundamental to ensuring diagnostic precision and research reproducibility. This investigation systematically discussed the evolutionary trajectory of occlusal analysis technologies through three key dimensions: technical principles, metric innovation, and efficacy evaluation framework. By conducting comparative analyses of various methodologies, we focus on delineating their unique technical advantages and clinical applicability boundaries. The study ultimately aims to establish evidence-based selection protocols for precision occlusal diagnostics while charting technical roadmaps for the iterative advancement of intelligent occlusal analysis systems.

Photoacoustic imaging (PAI), an emerging hybrid imaging modality, integrates the high spatial resolution of optical imaging with the deep-tissue imaging capability of ultrasound. Emerging evidence from recent studies in dental medicine has demonstrated the unique potential of PAI in screening, diagnosis, and therapeutic monitoring of oral pathologies, including periodontal diseases, oral cancers, and pulpal disorders. However, its clinical translation remains contingent upon addressing multiple technical barriers, particularly signal attenuation induced by optical scattering in biological tissues and limitations in real-time imaging efficiency. Therefore, investigating the advancements and challenges of PAI technology in the diagnosis of oral diseases will contribute to advancing further research and clinical applications, thereby providing practical support for improving the accuracy of early diagnosis and the efficacy of treatment for oral diseases.

Objective: To explore the effect of carboxylated polyamidoamine (PAMAM-COOH) in combination with magnesium ions on the remineralization ability of amorphous calcium phosphate (ACP) in inducing remineralization of dentin collagen fibers in a 50% ethanol solution. Methods: Forty-five intact third molars extracted for impaction reasons were obtained from the College & Hospital of Stomatology, Guangxi Medical University. Two types of demineralized dentin specimens were prepared: ①Fully demineralized dentin (n=30), specimens were immersed in 17% ethylenediaminetetraacetic acid (EDTA) (pH=7.4) at room temperature for 14 days with daily solution refreshment; ②Partially demineralized dentin (n=15), specimens were treated with 37% phosphoric acid gel (Ultra-Etch, Ultradent) for 15 seconds followed by thorough rinsing with deionized water. Three remineralization groups were established for demineralized dentin treatment: ①Control group, 50% ethanol solution; ②ACMP group, 50% ethanol solution containing amorphous magnesium calcium phosphate (ACMP); ③PAMAM-COOH/ACMP group, 50% ethanol solution incorporating carboxylated polyamidoamine dendrimer-modified ACMP (PAMAM-COOH/ACMP). The chemical composition of remineralization solutions was analyzed by Fourier-transform infrared spectrum (FTIR). The morphology and particle size distribution of nanoparticles were characterized using transmission electron microscope (TEM). The fully demineralized dentin specimens were treated with three different remineralization solutions (37 ℃ for 7 days) respectively. The mineralization of the dentin collagen fibers surface was observed using scanning electron microscope (SEM) and the distribution of minerals inside and outside the collagen fibers was examined by using TEM. The partially demineralized dentin specimens were treated with fluorescence-labeled remineralization solutions (37 ℃ for 7 days) respectively, followed by analysis using confocal laser scanning microscopy (CLSM) to quantitatively evaluate the penetration depth of the mineralization agents. Results: FTIR analysis confirmed the presence of characteristic absorption peaks corresponding to phosphate (PO₄^3-) groups, carbon-nitrogen bonds, and amide linkages in the PAMAM-COOH/ACMP nanocomposite. TEM observed that the PAMAM-COOH/ACMP nanoparticles exhibited an average particle size of (36.85±8.02) nm in an amorphous state. SEM observation indicates continuous mineral deposition on dentin collagen fibers in the PAMAM-COOH/ACMP group, while no mineral deposition in the control group and only minimal deposition in the ACMP group. TEM showed no mineral deposition inside or outside the collagen fibers in the control group, only external mineral deposition in the ACMP group, and high-density mineral deposition both inside and outside the fibers in the PAMAM-COOH/ACMP group. CLSM analysis revealed a statistically significant difference (P<0.05) in the dep

Objective: To evaluate the accuracy of the oral healthcare information provided by different large language models (LLM) to explore their feasibility and limitations in the application of oral auxiliary, treatment and health consultation. Methods: This study designed eight items comprising 47 questions in total related to the diagnosis and treatment of oral diseases [to assess the performance of LLM as an artificial intelligence (AI) medical assistant], and five items comprising 35 questions in total about oral health consultations (to assess the performance of LLM as a simulated doctor). These questions were answered individually by the five LLM models (Erine Bot, HuatuoGPT, Tongyi Qianwen, iFlytek Spark, ChatGPT). Two attending physicians with more than 5 years of experience independently rated the responses using the 3C criteria (correct, clear, concise), and the consistency between the raters was assessed using the Spearman rank correlation coefficient, and the Kruskal-Wallis test and Dunn post hoc test were used to assess the statistical differences between the models. Additionally, this study used 600 questions from the 2023 dental licensing examination to evaluate the time taken to answer, scores, and accuracy of each model. Results: As an AI medical assistant, LLM can assist doctors in diagnosis and treatment decision-making, with an inter-evaluator Spearman coefficient of 0.505 (P<0.01). As a simulated doctor, LLM can carry out patient popularization, with an inter-evaluator Spearman coefficient of 0.533 (P<0.01). The 3C scores of each model as an AI medical assistant and a simulated doctor were respectively: 2.00 (1.00, 3.00) and 2.00 (2.00, 3.00) points of Erine Bot, 1.00 (1.00, 2.00) and 2.00 (1.00, 2.00) points of HuatuoGPT, 2.00 (1.00, 2.00) and 2.00 (1.00, 3.00) points of Tongyi Qianwen, 2.00 (1.00, 2.00) and 2.00 (1.75, 2.25) points of iFlytek Spark, 3.00 (2.00, 3.00) and 3.00 (2.00, 3.00) points of ChatGPT (full score of 4 points). The Kruskal-Wallis test results showed that, as an AI medical assistant or a simulated doctor, there were statistically differences in the 3C scores among the five large language models (all P<0.001). The average score of the 5 LLMs on the dental licensing examination was 370.2, with an accuracy rate of 61.7% (370.2/600) and a time consumption of 94.6 min. Specifically, Erine Bot took 115 min, scored 363 points with an accuracy rate of 60.5% (363/600), HuatuoGPT took 224 min and scored 305 points with an accuracy rate of 50.8% (305/600), Tongyi Qianwen took 43 min, scored 438 points with an accuracy rate of 73.0% (438/600), iFlytek Spark took 32 min, scored 364 points with an accuracy rate of 60.7% (364/600), and ChatGPT took 59 min, scored 381 points with an accuracy rate of 63.5% (381/600). Conclusions: Based on the evaluation of LLM's dual roles as an AI medical assistant and a simulated doctor, ChatGPT performes the best, with basically correct

Objective: To evaluate the accuracy of a self-developed extraoral scanning system based on photogrammetry technology, and to provide evidence for advancing the development and clinical application evaluation of domestically produced scanning devices. Methods: This research group developed a photogrammetry-based implant extraoral scanning system with customized scan bodies. Two distinct edentulous implant resin models were designed and three-dimensional (3D)-printed by Center of Digital Dentistry, Peking University School and Hospital of Stomatology, containing 6 (Model 1) and 8 (Model 2) abutment analogs respectively. Reference data acquisition was performed using a high-precision denture 3D scanner with scan caps mounted on the analogs. Specialized scan bodies were then mounted on the analogs for 3D positional data acquisition using both the self-developed system (experimental group) and the clinically established system (control group). Each system conducted 10 repeated scans per model. Trueness was assessed through root mean square error (RMSE), linear deviation (LD), and angular deviation (AD) relative to reference data, while precision was determined through intra-group RMSE analysis. Systematic comparisons included inter-group performance on identical models and intra-group variability across different models. Results: For Model 1, the experimental group showed statistically significant advantages over controls in intra-group RMSE [(3.10±0.71) μm vs (4.61±1.51) μm, P<0.001], reference-data RMSE [(21.48±0.60) μm vs (32.50±0.63) μm, P<0.001], linear deviation [23.64 (32.35) μm vs 44.86 (55.73) μm, P<0.001], and angular deviation [0.29° (0.29°) vs 0.23° (0.33°), P<0.001]. In Model 2, significant improvements were observed in intra-group RMSE [(4.47±1.58) μm vs (6.21±2.07) μm, P<0.001], reference-data RMSE [(38.84±0.86) μm vs (43.69±1.34) μm, P<0.001], and linear deviation [37.95 (50.68) μm vs 49.71 (58.89) μm, P<0.001]. Both groups exhibited model-dependent variability, with RMSE of precision and trueness of both groups, linear deviation of experimental group, angular deviation of control group showing statistically significant increases (all P<0.001) corresponding to abutment analog quantity. Conclusions: The self-developed scanning system demonstrates superior accuracy in 3D positional acquisition of abutment analogs compared to the contral group system, with implant number identified as a critical determinant of extraoral scanning accuracy.