Cone Beam Computed Tomography (CBCT) is widely used in dentistry; however, many practitioners have limited knowledge regarding its appropriate indications and radiation safety protocols. Optimizing CBCT use is essential for both patient safety and diagnostic efficacy. This study aimes to evaluate Turkish dentists’ and dental specialists’ awareness, attitudes, and practices regarding CBCT usage, radioprotection, and dose-reduction measures. A nationwide cross-sectional study was conducted from November 2021 to February 2024, involving 306 volunteer dentists with 1 to 51 years of clinical experience. Participants completed an 18-item questionnaire covering CBCT usage frequency, imaging indications, technical parameter selection, radiation protection and training. Statistical analysis included Chi-square and Kruskal-Wallis tests. Implant planning, lesion evaluation, and root morphology assessment were the most common reasons for CBCT use. Oral and maxillofacial radiologists used CBCT more frequently than other specialists. A significant relationship was observed between frequency of CBCT use and the selection of technical parameters (voxel size, field of view) (p < 0.001). While lead apron usage was common, thyroid collar usage varied and showed a significant relationship with scan frequency (p = 0.001). Most participants relied on automatic settings and did not consistently consult a radiologist either before or after imaging. The study reveals knowledge gaps in CBCT usage and radiation protection practices among dental professionals. Ongoing professional education and clearer guidelines are essential to ensure safe and effective use of CBCT technology. • A total of 306 dentists and dental specialists participated in this nationwide survey. • Implant planning, pathology, and root morphology were the top reasons for CBCT use. • A significant correlation was found between CBCT frequency and technical parameter selection. • Thyroid collar usage was significantly associated with CBCT acquisition frequency. • Knowledge gaps remain regarding radiation protection and consultation with radiologists.

Purpose: The aim of this study is to compare the efficacy of artificial intelligence use in oral radiology learning in the undergraduate dental students. Materials&Methods: Fifty third-year students in the University of Lokman Hekim were detected images with the artificial intelligence method (AI) and standard lecture method (SL) for anatomical landmarks in panoramic radiographs. SL consisted of a frontal lecture through a standardized presentation. CranioCatch model (Eskisehir, Turkey) was used as deep learning-based artificial intelligence model. One panoramic image was loaded to the application and anatomic landmarks were detected by teacher, students were asked to mark. AI recorded and scored students answers. A questionnaire study was conducted for the perception of students in terms of validity and reliability regarding assessment and evaluation for each methods. Results: 50 undergraduate students (26 female,24 male) answered 7questions, 5-point Likert type. The conformity to the normal distribution was evaluated with the Shapiro-Wilk test and the graphical approach (Normal Q-Q Plot). The values did not conform to the normal distribution. As a result of the reliability analysis performed for the measurement tool, the Cronbach’s Alpha coefficient was found 0.828. Wilcoxon Test was used to test the significance of the difference between each methods. There is a statistically significant difference between the mean values of evaluation measurements(p=0.014). AI was higher than the mean of evaluation measurement values compared to SL. Conclusion: AI models have performed very well in measurement and evaluation in oral radiology learning.

Amaç: Yapay zekâ, diş hekimliği alanında klinik uygulamalarda hızla kendine yer bulmuş ve çeşitli alanlarda önemli katkılar sağlamıştır. Klinik kullanımının getirdiği faydaların yanı sıra diş hekimliği eğitimi bağlamında otomatik öğrenme sistemleriyle desteklenen ve yapay zeka tabanlı yazılımlar, halen gelişime açık ve potansiyel vaat eden bir alandır. Diş hekimliği eğitimine yapay zekanın entegrasyonu eğitici ve öğrenciler açısından faydalar sağlayan güncel ve inovatif bir yaklaşımdır. Diş hekimliği fakültelerinde geleneksel eğitim modellerini dönüştürme potansiyeline sahip olan yapay zekâ tabanlı yaklaşımlar, öğrenme kalitesini artırmak ve öğrenci başarısını desteklemek amacıyla zeki öğretim sistemlerini devreye sokmaktadır. Bu durum, diş hekimliği eğitiminde öğrenme süreçleri, öğretme, ölçme-değerlendirme ve geri bildirim süreçlerinin gözden geçirilmesine ve hatta köklü değişimlere kapı aralayabilecek bir potansiyele sahiptir. Yöntem: Bu derleme, geleneksel bir yöntemle hazırlanmış olup, diş hekimliği eğitiminde yapay zekâ uygulamalarının mevcut durumu ve potansiyel etkilerini incelemektedir. Son zamanlarda yapay zekanın hızla gelişmesiyle birlikte literatürde tıp eğitimi alanında da yaygın kullanımına ilişkin yayınlar artmaktadır. Mezuniyet öncesi eğitim öğretimde, müfredat içeriğinde, ölçme değerlendirmede, üç boyutlu sanal eğitim ortamları yaratılmasında ve diş hekimliği eğitiminin gelecek perspektifleri açısından yapay zekanın getirdiği yenilikler vurgulanmıştır. Yapay zekanın diş hekimliği eğitimindeki yeri eğiticiler, öğrenciler ve eğitim sistemleri açısından literatür örnekleriyle paylaşılmıştır. Bulgular: Tıp eğitiminde yapay zekâ kullanımı, sağlık alanında etkin teorik ve pratik eğitim açısından sürekli bir dönüşüm geçirerek kapsamını genişletmektedir. Yapay zekâ destekli uygulama ve yazılımlar ile sanal gerçeklik simülatörlerinden haptik cihazlara, robotik hastalara kadar pek çok inovatif yenilik, diş hekimliği eğitiminin zorlu klinik öncesi ve klinik eğitim süreçlerine hızla entegre olmaktadır. Bu teknolojiler, öğrencilerin beklenen motor beceri seviyesine daha kısa sürede ulaşmalarını sağlamakta ve klinik öncesi dönemde gerçek hasta deneyimine benzer çalışmalar yapmalarına olanak tanımaktadır. Klinik dönemde ise yapay zekâ tabanlı sistemler klinik hataları azaltarak güvenli dental uygulamalar yapılmasına, hasta bulgularının analizinde, tedavi planlamasında karar vermede yardımcı olmakta böylece tedavi kalitesini artırmaktadır. Bu teknolojilerin eğitim, müfredat geliştirilmesi, ölçme değerlendirilmesi gibi süreçlerde kullanımı, hem eğiticiler hem de öğrenciler açısından diş hekimliği eğitiminin ilerlemesine önemli katkılar sunmaktadır. Öğrencilerin ve eğiticilerin bu teknolojileri kabul edilebilir bulması, eğitim süreçlerinde yapay zekânın etkinliğini artıran başka bir önemli faktördür. Sonuç: Diş hekimliği öğrencilerinin ve eğiticilerin yapay zekâ destekli uygulamalar konusunda etkin birer kullanıcı olmaları, hem meslektaşlarının hem de hastalarının eğitimi konusunda önemli bir rol oynamalarını gerektirmektedir. Özellikle yapay zekâ tabanlı teknolojilerin kullanıldığı durumlarda, öğrencilerin, hasta yönetiminde yüz yüze deneyim kazanmaları oldukça önemli bir faktördür. Yapay zeka tabanlı uygulamaların kullanıldığı durumlar, yapay zekanın diş hekimliği eğitimindeki yeri, avantaj ve dezavantajları, kısıtlılıkları tartışılmıştır. Yapay zekanın diş hekimliği eğitiminde aktif kullanılması, öğrenci merkezli öğrenmeye yönelik olarak yenilikçi bir yaklaşım sağlamaktadır. Eğitime yapay zekanın entegrasyonu hem diş hekimliğinde mezuniyet öncesi eğitiminde hem de yaşam boyu öğrenmede gelecekte klinik uygulamalarda inovatif teknolojilerin etkin bir şekilde kullanılmasını sağlayacaktır.

Objectives: Wound dressings protect the wound and accelerate the healing of ulcers. This study was designed to assess the impact of distinct the impact of oral dressings for the wound healing phase of experimentally produced traumatic oral ulcers.Materials and Methods: The study included 45 male Sprague-Dawley rats, which were randomly allocated into three groups: Group 1, Group 2 and Control Group.Two different wound dressings were applied: hyaluronic acid (Group 1) and a wound dressing composed of a combination of bioactive materials (Group 2). Tissue samples were taken in the 3rd, 7th,14th days and evaluated histopathologically. The Kruskal–Wallis test was applied to compare numerical data across groups, followed by post-hoc testing to identify intergroup differences. Categorical variables were compared using the Chi-square test.Results: Group 1 exhibited enhanced overall efficacy, with a notable decrease in ulceration recorded on days 3, 7, and 14 in Group 2. Edema was more noticeable in Group 2 than in Group 1. Group 2 revealed superior efficacy in facilitating granulation tissue production in early phase. Both groups demonstrated enhanced migration of polymorphonuclear leukocytes and mononuclear cells to the ulcer location. Conclusions: Both wound coverings positively affected the wound healing process by regulating distinct phases of healing.

Objective: This study aimed to evaluate whether radiopacity differs among additively manufactured permanent resins, temporary resins, and conventional resin‐based composites at clinically relevant thicknesses. Materials and Methods: Eight dental materials were evaluated and categorized into four groups: additively manufactured permanent resins (VarseoSmile Crown Plus, Saremco Print CrownTec), additively manufactured temporary resins (Arma Resin TEMP Teeth, Alias Temp C&B), conventional self‐cure resin‐based composites (Stela Automix, Stela Capsule), and conventional light‐cure resin‐based composites (Gradia Posterior, G‐ænial Injectable). Rectangular specimens (5 × 5 mm) were fabricated at two standardized thicknesses (1 and 2 mm) ( n = 10). Extracted human tooth sections and a 21‐step aluminum wedge were used as reference standards. Radiographic images were obtained using a digital intraoral radiography system, and radiopacity values were measured in mmAl using ImageJ software. Statistical analysis was performed using Kruskal–Wallis, Dunn–Bonferroni, and Mann–Whitney U tests ( α = 0.05). Results: Significant differences in radiopacity were observed among the tested materials ( p < 0.05). Additively manufactured temporary resins demonstrated the lowest radiopacity values, whereas conventional resin‐based composites exhibited the highest radiopacity. Permanent additively manufactured resins showed intermediate radiopacity values. Increasing specimen thickness from 1 to 2 mm significantly increased radiopacity values for all materials ( p < 0.001). Several temporary additively manufactured materials presented radiopacity values lower than dentin and enamel. Conclusion: The findings suggest that radiopacity varies significantly among restorative materials and is influenced by specimen thickness. In particular, the relatively low radiopacity of some additively manufactured resins may limit their radiographic detectability, which should be considered during material selection in clinical practice. Clinical Significance: Radiopacity differences among restorative materials may influence the detection of recurrent caries and restoration margins. Lower radiopacity of some additively manufactured resins highlights the importance of careful material selection for accurate radiographic diagnosis.

The most prevalent cause of tooth fractures in pets is trauma. Direct pulp exposure necessitates immediate extraction or root canal treatment. Monolithic zirconia are used as crown material in dentistry. It is reported a case of a 4-year-old female Ankara cat who had a monolithic zirconia crown restored following root canal therapy in the current study. An trauma to the cat's maxillary canine tooth resulted in a fracture that involved the pulp tissue. The tooth underwent root canal therapy and prepararation was done for fixed monolithic zirconia prosthetic full crown restoration. Maxillofacial and traumatic dentoalveolar injuries can decrease oral function, produce pain and inflammation, and negatively affect a cat's quality of life. Monolithic zirconia crowns are a new, significant restorative material that has been used rarely and little studied in veterinary dentistry despite having important material properties.