International dental journal最新文献

{"title":"Deep Learning Based on Swin-Transformer and 3D U-Net: Implant Three-Dimensional Position Planning.","authors":"Jiajin Shen, Xi Yang, Junbiao Zhang, Xiaohuan Lu, Guangneng Chen, Yin Liu, Bin Ye, Minxian Ma","doi":"10.1016/j.identj.2026.109690","DOIUrl":"10.1016/j.identj.2026.109690","url":null,"abstract":"<p><strong>Introduction and aims: </strong>This study aimed to devise a deep learning-based model for the automated identification of anatomical mandibular lingual concavities in the posterior mandible and the prediction of biologically guided three-dimensional implant positions.</p><p><strong>Methods: </strong>Cone-beam computed tomography (CBCT) images with single-tooth posterior mandibular edentulism were included. A deep learning framework was constructed utilizing 3D U-Net and Swin-Transformer. This framework was designed to perform automated segmentation of teeth, mandible, and the mandibular nerve canal; to classify morphological types of lingual concavities; and to identify implant key points with coordinate prediction. Model performance was assessed via five-fold cross-validation.</p><p><strong>Results: </strong>The proposed model achieved Dice similarity coefficients ranging from 0.87 to 0.91 for dental segmentation. In the classification of mandibular lingual concavities, an accuracy of 0.92 to 0.97 was attained. Regarding the prediction of three-dimensional implant positions, the automatically generated plans maintained a safety margin of 3.20 to 3.89 mm from the mandibular nerve canal. Furthermore, sufficient bone volume was preserved at both cervical and apical implant levels, with buccal/lingual cervical bone widths averaging 4.94 to 5.78 mm.</p><p><strong>Conclusion: </strong>The deep learning model in this experiment performed well across different views and learning tasks in a retrospective internal validation setting. Furthermore, it demonstrated the ability to accurately identify relevant anatomical structures, and the predicted three-dimensional implant positions showed clinically acceptable safety margins in the internal validation cohort.</p><p><strong>Clinical relevance: </strong>This model provides a preliminary AI-assisted framework for identifying critical mandibular anatomical structures and generating biologically guided preliminary implant position suggestions, thereby helping to mitigate intraoperative complications and reduce the risk of postoperative mechanical and biological complications in preclinical evaluation.</p>","PeriodicalId":13785,"journal":{"name":"International dental journal","volume":"76 4","pages":"109690"},"PeriodicalIF":5.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13320490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148329128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Explainability: The Trust Paradox of Conversational Artificial Intelligence in Endodontics.","authors":"Mohammed Turky, Paul Mh Dummer","doi":"10.1016/j.identj.2026.109696","DOIUrl":"10.1016/j.identj.2026.109696","url":null,"abstract":"","PeriodicalId":13785,"journal":{"name":"International dental journal","volume":"76 4","pages":"109696"},"PeriodicalIF":5.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13312132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148277413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Deep Learning Models for Identifying Maxillary Transverse Deficiency from Intraoral Photographs.","authors":"Jianing Li, Rui Wang, Zhou Yi, Jianhui Ni, Zhigang Zuo, Yue Wang","doi":"10.1016/j.identj.2026.109689","DOIUrl":"10.1016/j.identj.2026.109689","url":null,"abstract":"<p><strong>Introduction and aims: </strong>Maxillary transverse deficiency (MTD) is conventionally evaluated using cone-beam computed tomography (CBCT), which entails increased radiation exposure, cost, and clinical workload. Frontal intraoral photographs are routinely obtained in orthodontic practice. Using CBCT-derived transverse measurements as reference standards, we developed, validated, and compared multiple deep learning (DL) models to assess the feasibility of identifying MTD from frontal intraoral photographs.</p><p><strong>Methods: </strong>This study included 826 internal and 192 external patients who underwent paired frontal intraoral photographs and CBCT. MTD was determined based on the University of Pennsylvania analysis (UPA) and Yonsei transverse analysis (YTA) labels. DenseNet 121, ResNet 18, EfficientNet B0/B3, and MobileNetV3 Small/Large were trained separately on photographs using UPA- and YTA-based labels. Five-fold cross-validation was employed, and performance was evaluated on the internal and external test sets using accuracy, precision, recall, and F1 scores, along with confusion matrices and areas under the receiver operating characteristic curves. DeLong's test assessed the differences between the models.</p><p><strong>Results: </strong>In the external test set under the UPA labelling scheme, ResNet 18 achieved the highest accuracy (90.62%). Under the YTA labelling scheme, DenseNet 121 and ResNet 18 achieved the highest accuracy (96.88%). Across all internal and external test sets using both labelling schemes, DenseNet 121 and ResNet 18 yielded the best overall performance, and no statistically significant difference was observed between the two models (P > .05).</p><p><strong>Conclusions: </strong>The DL models demonstrated strong potential for analysing frontal intraoral photographs to detect MTD. These findings provide initial insights into the use of DL models to identify MTD from frontal intraoral photographs for orthodontic purposes.</p><p><strong>Clinical relevance: </strong>This study demonstrates the feasibility of using DL-based recognition of frontal intraoral photographs to identify MTD. As a cost-effective adjunctive tool, the proposed approach may assist clinicians in identifying MTD and help improve case selection for CBCT imaging.</p>","PeriodicalId":13785,"journal":{"name":"International dental journal","volume":"76 4","pages":"109689"},"PeriodicalIF":5.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13314740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148283599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IL-6/STAT3-Mediated miR-181a-5p in Bone Marrow-Derived Mesenchymal Stem Cells Regulates Th17/Treg Balance in Experimental Periodontitis.","authors":"Si Wu, Jie Wang, Deqin Yang","doi":"10.1016/j.identj.2026.109675","DOIUrl":"10.1016/j.identj.2026.109675","url":null,"abstract":"<p><strong>Objective: </strong>Periodontitis adversely affects oral and overall health. Bone marrow-derived mesenchymal stem cells (BMMSCs) play regulatory roles in the immune system, and the exploration of therapeutic strategies involving BMMSCs has garnered significant attention in the context of inflammatory pathological processes. Interleukin-6 can regulate microRNA expression by activating the signal transducer and activator of transcription 3 (STAT3) and functions in some systemic diseases. Notably, the regulatory mechanisms between IL-6 and miR-181a-5p in BMMSCs underlying periodontitis are still unknown. This study aims to uncover the regulatory mechanisms between IL-6 and miR-181a-5p and create a new treatment strategy for periodontal tissue regeneration.</p><p><strong>Methods: </strong>In this study, Western blot was used to assess IL-6 and STAT3 levels. Quantitative real-time polymerase chain reaction assessed the expression of miR-181a-5p and the relative genes. Enzyme-linked immunosorbent assay detected the content of IL-6. The Cell Counting Kit-8 was used to evaluate cell viability. Alkaline phosphatase staining was used to analyse the osteogenesis of cells. Chromatin immunoprecipitation and dual luciferase activity assays were used to verify the binding relationship between miR-181a-5p, STAT3, and IL-6. Flow cytometry was used for identifying cell types. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology analysed potential signal pathways of genes targeted by miR-181a-5p. In addition, the present research was further investigated using a ligature-induced periodontitis model in mice. The micro-computed tomography, haematoxylin and eosin, and tartrate-resistant acid phosphatase (TRAP) assays were used for histologic analysis of the degree of inflammation and bone recovery in the experimental periodontitis and treatment group.</p><p><strong>Results: </strong>IL-6 and miR-181a-5p were upregulated in experimental periodontitis in mice. The IL-6 recombinant protein promoted mBMMSC proliferation and upregulated miR-181a-5p levels via STAT3. Functional inhibition of miR-181a-5p alleviated the T helper 17 (Th17) cell/regulatory T cell (Treg) immune imbalance triggered by IL-6 stimulation. Injection of miR-181a-5p inhibitor preconditioned mBMMSCs significantly promoted periodontal tissue regeneration, and osteoclasts and bone resorption were reduced significantly.</p><p><strong>Conclusion: </strong>IL-6 may contribute to the progression of experimental periodontitis in mice via modulating STAT3‑mediated miR‑181a‑5p expression in mBMMSCs and may potentially influence periodontal tissue regeneration through regulating the Th17/Treg balance.</p>","PeriodicalId":13785,"journal":{"name":"International dental journal","volume":"76 4","pages":"109675"},"PeriodicalIF":5.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13276558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148224812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to 'AI-Driven Segmentation of Three Oral Diseases: Enabling Precision Diagnosis and Decision Support' [International Dental Journal, Volume 76, Issue 3, June 2026, 109486].","authors":"Baowen Cheng, Meijuan Xiang, Zhuofan Zhao, Xiaodong Zhang, Yu-Feng Wang, Shuyun Ge, Fang Zhao, Meng Li, Haiyan Wang","doi":"10.1016/j.identj.2026.109684","DOIUrl":"10.1016/j.identj.2026.109684","url":null,"abstract":"","PeriodicalId":13785,"journal":{"name":"International dental journal","volume":"76 4","pages":"109684"},"PeriodicalIF":5.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13279163/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148251520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When Artificial Intelligence Agrees Too Easily: The Emerging Risk of Artificial Intelligence Sycophancy in Dentistry.","authors":"Mohammed Turky, Paul M H Dummer","doi":"10.1016/j.identj.2026.109706","DOIUrl":"10.1016/j.identj.2026.109706","url":null,"abstract":"","PeriodicalId":13785,"journal":{"name":"International dental journal","volume":"76 4","pages":"109706"},"PeriodicalIF":5.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13319878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148307927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Efficacy of Mesenchymal Stem Cell-Based Therapy for Sjögren's Syndrome: A Systematic Review and Meta-Analysis of Preclinical Studies.","authors":"Xinyue Yang, Cheng Shi, Zhuoyang Wu, Jiahao Hu, Aokang Yao, Sheng Luo, Bo Fu, Yaqiong Yu","doi":"10.1016/j.identj.2026.109688","DOIUrl":"10.1016/j.identj.2026.109688","url":null,"abstract":"<p><strong>Background: </strong>Sjögren's syndrome (SjS) is a chronic autoimmune disease primarily characterized by xerostomia, often accompanied by xerophthalmia, cutaneous dryness, arthralgia, and stiffness. Mesenchymal stem cell (MSC)-based therapies have shown promising immunomodulatory potential in autoimmune diseases, yet their efficacy in SjS remains uncertain. Before advancing to clinical application, a systematic evaluation of preclinical evidence is essential to clarify their therapeutic impact and experimental consistency.</p><p><strong>Methods: </strong>This systematic review and meta-analysis were registered in PROSPERO (CRD42023471348) and conducted in accordance with PRISMA guidelines. Comprehensive searches were performed in PubMed, Embase, Scopus, Cochrane Library, and Web of Science up to 1 May 2025. Random-effects meta-analyses were conducted to assess changes in salivary flow rate (SFR), inflammatory infiltration, and cytokine expression.</p><p><strong>Results: </strong>A total of 25 studies met the inclusion criteria. Pooled analyses demonstrated that MSCs, MSC-derived exosomes, and MSC-conditioned medium significantly improved stimulated SFR (standardized mean difference = 3.19, 95% confidence interval 2.50-3.88, P < .001) and reduced inflammatory infiltration (standardized mean difference = -2.04, 95% confidence interval -2.66 to -1.43, P < .001). MSC-based therapies decreased serum levels of proinflammatory cytokines (interleukin-6 [IL-6] and interferon-gamma) and increased IL-10. Exploration of heterogeneity indicated that MSC type and dosage influenced SFR outcomes, MSC intervention type affected inflammatory infiltration and serum IL-6 levels, and MSC dosage influenced serum IL-10 levels.</p><p><strong>Conclusion: </strong>MSC-based therapies show potential for treating SjS in animal models, improving salivary secretion, reducing inflammation, and modulating immune cytokines. Substantial heterogeneity across studies highlights the need for standardized protocols regarding MSC source, dosage, and treatment duration before clinical translation.</p>","PeriodicalId":13785,"journal":{"name":"International dental journal","volume":"76 4","pages":"109688"},"PeriodicalIF":5.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13314907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148301634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Perceived Profile Attractiveness From Cephalometric Measurements Using Machine Learning.","authors":"Zeynab Pirayesh, Fatemeh Sohrabniya, Seyed AmirHossein Ourang, Hossein Mohammad-Rahimi, Saharnaz Esmaeili, Sarvin Khosravani, Saeed Reza Motamedian, Falk Schwendicke, Antonin Tichy","doi":"10.1016/j.identj.2026.109679","DOIUrl":"10.1016/j.identj.2026.109679","url":null,"abstract":"<p><strong>Objectives: </strong>Orthodontic treatment planning requires careful consideration of facial aesthetics. This study aimed to compare machine learning (ML) models to predict perceived attractiveness based on lateral cephalometric measurements and to identify parameters that significantly impact perceived attractiveness.</p><p><strong>Methods: </strong>Lateral cephalometric radiographs and corresponding facial photographs of 400 patients were utilized in this study. Twenty-six raters, including 20 laypeople and 6 domain experts (art, maxillofacial surgery, and orthodontics), evaluated each photograph using a 1 to 5 Visual Analogue Scale (VAS), and cephalometric analyses using the Dolphin software served as the reference standard. Five ML models, linear regression, Support Vector Machine, XGBoost Regressor, Random Forest, and artificial neural network, were trained to predict attractiveness scores based on the reference standard, and their performance on a test set (n = 59) was evaluated using root mean square error (RMSE). Subsequently, feature importance analysis was conducted to identify cephalometric indices that had the largest impact on VAS scores.</p><p><strong>Results: </strong>Experts consistently rated facial attractiveness higher than laypeople, especially in age groups >15 years. Furthermore, female raters rated female profiles significantly higher and male profiles significantly lower than male raters, with differences evident in >15 age groups. Among the 5 ML models, Random Forest demonstrated the best performance (RMSE = 0.50). The feature importance analysis revealed that soft tissue parameters, especially those related to facial convexity, maxillary and mandibular prognathism, and vertical proportions, were most predictive of attractiveness scores.</p><p><strong>Conclusion: </strong>ML models showed promising results in predicting facial attractiveness based on cephalometric measurements and highlighted the correlation of facial aesthetics with soft tissue parameters. The proposed ML-based approach can aid clinicians in the objective assessment of treatment outcomes.</p>","PeriodicalId":13785,"journal":{"name":"International dental journal","volume":"76 4","pages":"109679"},"PeriodicalIF":5.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13319888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148316964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Algorithms: Embodied Artificial Intelligence and the Future of Dentistry.","authors":"Mohammed Turky, Paul M H Dummer","doi":"10.1016/j.identj.2026.109703","DOIUrl":"10.1016/j.identj.2026.109703","url":null,"abstract":"","PeriodicalId":13785,"journal":{"name":"International dental journal","volume":"76 4","pages":"109703"},"PeriodicalIF":5.2,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148277394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to 'Single vs Triple-Headed Toothbrushes in Children With Special Needs: A Randomised Crossover Trial of Brushing Efficacy' [International Dental Journal, Volume 76, Issue 2, April 2026, 109388].","authors":"Thanapon Tanaiadchawoot, Apiwan Smutkeeree, Sudaporn Khamduangdao, Pornpailin Kasemkhun","doi":"10.1016/j.identj.2026.109569","DOIUrl":"10.1016/j.identj.2026.109569","url":null,"abstract":"","PeriodicalId":13785,"journal":{"name":"International dental journal","volume":"76 3","pages":"109569"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13121406/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147770578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}