Background: This study aimed to determine the volumetric size of the maxillary sinus and investigate the effect of gender and age on maxillary sinus volume (MSV) using cone-beam computed tomography (CBCT) images in a Turkish subpopulation. Materials and methods: This retrospective volumetric CBCT study was carried out on 133 individuals (84 females, 49 males) between 8 and 51 years old. MSV was measured using the MIMICS 21.0 software (Materialise HQ Technologielaan, Leuven, Belgium). All statistical analyses were performed using the SPSS 21.0 (SPSS, Chicago, IL, USA) software. Mean and standard deviation of both maxillary sinuses measurements were calculated and compared to gender and age. P values < 0.05 were considered to indicate statistical significance. Results: Mean volume of the right maxillary sinus was 13.173 cm3, while for the left was 13.194 cm3. There was no significant difference between right and left maxillary sinus volumes (p > 0.05). There was no significant correlation between MSV and age (p > 0.05). It was found that MSV did not change according to gender (p > 0.05). Conclusions: Right and left maxillary sinus volumes were not different from each other. Gender and age were not found to be related to maxillary sinus volume. Background: This study aimed to determine the volumetric size of the maxillary sinus and investigate the effect of gender and age on maxillary sinus volume (MSV) using cone-beam computed tomography (CBCT) images in a Turkish subpopulation. Materials and methods: This retrospective volumetric CBCT study was carried out on 133 individuals (84 females, 49 males) between 8 and 51 years old. MSV was measured using the MIMICS 21.0 software (Materialise HQ Technologielaan, Leuven, Belgium). All statistical analyses were performed using the SPSS 21.0 (SPSS, Chicago, IL, USA) software. Mean and standard deviation of both maxillary sinuses measurements were calculated and compared to gender and age. P values < 0.05 were considered to indicate statistical significance. Results: Mean volume of the right maxillary sinus was 13.173 cm3, while for the left was 13.194 cm3. There was no significant difference between right and left maxillary sinus volumes (p > 0.05). There was no significant correlation between MSV and age (p > 0.05). It was found that MSV did not change according to gender (p > 0.05). Conclusions: Right and left maxillary sinus volumes were not different from each other. Gender and age were not found to be related to maxillary sinus volume.

This study evaluates and compares the performance of ChatGPT-3.5, ChatGPT-4 Omni (4o), Google Bard, and Microsoft Copilot in responding to text-based multiple-choice questions related to oral radiology, as featured in the Dental Specialty Admission Exam conducted in Türkiye. A collection of text-based multiple-choice questions was sourced from the open-access question bank of the Turkish Dental Specialty Admission Exam, covering the years 2012 to 2021. The study included 123 questions, each with five options and one correct answer. The accuracy levels of ChatGPT-3.5, ChatGPT-4o, Google Bard, and Microsoft Copilot were compared using descriptive statistics, the Kruskal-Wallis test, Dunn’s post hoc test, and Cochran’s Q test. The accuracy of the responses generated by the four chatbots exhibited statistically significant differences (p = 0.000). ChatGPT-4o achieved the highest accuracy at 86.1%, followed by Google Bard at 61.8%. ChatGPT-3.5 demonstrated an accuracy rate of 43.9%, while Microsoft Copilot recorded a rate of 41.5%. ChatGPT-4o showcases superior accuracy and advanced reasoning capabilities, positioning it as a promising educational tool. With regular updates, it has the potential to serve as a reliable source of information for both healthcare professionals and the general public. Not applicable.

Tassoker M, et al. Imaging Sci Dent. 2018 Sep;48(3):201-212. https://doi.org/10.5624/isd.2018.48.3.201

Terlemez A, et al. Imaging Sci Dent. 2019 Jun;49(2):115-122. https://doi.org/10.5624/isd.2019.49.2.115

Objectives: The aim of the present study was to compare five convolutional neural networks for predicting osteoporosis based on mandibular cortical index (MCI) on panoramic radiographs. Methods: Panoramic radiographs of 744 female patients over 50 years of age were labeled as C1, C2, and C3 depending on the MCI. The data of the present study were reviewed in different categories including (C1, C2, C3), (C1, C2), (C1, C3), and (C1, (C2 +C3)) as two-class and three-class predictions. The data were separated randomly as 20% test data, and the remaining data were used for training and validation with fivefold cross-validation. AlexNET, GoogleNET, ResNET-50, SqueezeNET, and ShuffleNET deep-learning models were trained through the transfer learning method. The results were evaluated by performance criteria including accuracy, sensitivity, specificity, F1-score, AUC, and training duration. The Gradient-Weighted Class Activation Mapping (Grad-CAM) method was applied for visual interpretation of where deep-learning algorithms gather the feature from image regions. Results: The dataset (C1, C2, C3) has an accuracy rate of 81.14% with AlexNET; the dataset (C1, C2) has an accuracy rate of 88.94% with GoogleNET; the dataset (C1, C3) has an accuracy rate of 98.56% with AlexNET; and the dataset (C1,(C2+C3)) has an accuracy rate of 92.79% with GoogleNET. Conclusion: The highest accuracy was obtained in the differentiation of C3 and C1 where osseous structure characteristics change significantly. Since the C2 score represent the intermediate stage (osteopenia), structural characteristics of the bone present behaviors closer to C1 and C3 scores. Therefore, the data set including the C2 score provided relatively lower accuracy results.