Investigation of coronoid process hyperplasia using Levandoski analysis on panoramic radiographs

BACKGROUND The diagnosis of coronoid process hyperplasia(CPH)is usually based on symptoms and radiological imaging.Because of its similar symptoms,it can be confused with temporomandibular joint diseases.Therefore,an objective and reproducible way of diagnosis should be determined.AIM To investigate CPH using Levandoski analysis on panoramic radiographs to determine its prevalence.METHODS A total of 300 panoramic radiograph images(600 coronoid processes)were examined.Having measured the Condyle-Gonion(Cd-Go)and Coronoid-Gonion(Cor-Go)distances,the Cor-Go:Cd-Go ratio was calculated for the left and right sides of each image.RESULTS There was a statistically significant difference in Cd-Go and Cor-Go distances between male and female participants(P<0.001).There was no statistically significant relationship between Cor-Go:Cd-Go ratios and gender(P>0.05).CONCLUSION Cd-Go and Cor-Go distances were statistically significantly increased in males on both the left and right sides.The ratio of Cor-Go:Cd-Go was preserved in both genders.The prevalence of CPH was found to be 0.3%.

Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs

Panoramic radiographs can assist dentist to quickly evaluate patients’overall oral health status.The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify pathology,and also plays a key role in an automatic diagnosis system.However,the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist,while the interpretation of panoramic radiographs might lead misdiagnosis.Therefore,it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs.In this study,SWin-Unet,the transformer-based Ushaped encoder-decoder architecture with skip-connections,is introduced to perform panoramic radiograph segmentation.To well evaluate the tooth segmentation performance of SWin-Unet,the PLAGH-BH dataset is introduced for the research purpose.The performance is evaluated by F1 score,mean intersection and Union(IoU)and Acc,Compared with U-Net,Link-Net and FPN baselines,SWin-Unet performs much better in PLAGH-BH tooth segmentation dataset.These results indicate that SWin-Unet is more feasible on panoramic radiograph segmentation,and is valuable for the potential clinical application.

Transfer learning-based super-resolution in panoramic models for predicting mandibular third molar extraction difficulty: a multi-center study

Background:This study aims to predict the extraction difficulty of mandibular third molars based on panoramic images using transfer learning while employing super-resolution(SR)technology to enhance the feasibility and validity of the prediction.Methods:We reviewed a total of 608 preoperative mandibular third molar panoramic radiographs from two medical facilities:the First Affiliated Hospital of Zhengzhou University(n=509;456 in the training set and 53 in the test set)and the Henan Provincial Dental Hospital(n=99 in the validation set).We conducted a deep-transfer learning network on high-resolution(HR)panoramic radiographs to improve the longitudinal resolution of the images and obtained the SR images.Subsequently,we constructed models named Model-HR and Model-SR using high-dimensional quantitative features extracted through the Least Absolute Shrinkage and Selection Operator method.The models’performances were evaluated using the receiver operating characteristic curve(ROC).To assess the reliability of the model,we compared the results from the test set with those of three dentists.Results:Model-SR outperformed Model-HR(area under the curve(AUC):0.779,sensitivity:85.5%,specificity:60.9%,and accuracy:79.8%vs.AUC:0.753,sensitivity:73.7%,specificity:73.9%,and accuracy:73.7%)in predicting the difficulty of extracting mandibular third molars.Both Model-HR(AUC=0.821,95%CI 0.687–0.956)and Model-SR(AUC=0.963,95%CI 0.921–0.999)demonstrated superior performance compared to expert dentists(highest AUC=0.799,95%CI 0.671–0.927).Conclusions:Model-SR yielded superior predictive performance in determining the difficulty of extracting mandibular third molars when compared with Model-HR and expert dentists’visual assessments.

Extra-Oral Digital Panoramic Radiographic Interpretation and Clinical Findings of Stage IV Periodontitis among Some Samples Concerning Saudi/Non-Saudi Nationality

Background: There are associations between the severity of periodontal diseases and some predisposing factors, such as age, race, and income rate. Therefore, the present study was designed to evaluate the extra-oral digital panoramic radiographic interpretation and clinical findings of stage IV periodontitis among some samples concerning Saudi/non-Saudi nationality. Material and Methods: The study comprised a random sample of 298 residents in Saudi Arabia referred to the internship program clinics at the College of Dentistry, King Khalid University. They were according to the age of participants as follows: youth (15 - 24 years), adults (25 - 64 years), and seniors (65 years and over) within two groups according to nationality (Saudi and non-Saudi). Clinical periodontal examination and radiographic extra oral digital panoramic radiographs were done, and then the periodontal parameters, the percentage of bone loss, and the pattern of bone loss were recorded. The data was collected, and the statistical analysis was performed with ANOVA test, Tukey’s test, Chi-square test, and T-test and considered a P-value at (p Results: The samples included 62 (20%) youth participants with mean of age (21.806 ± 3.390), 182 (57%) adults’ participants with mean age (42.802 ± 9.094), and 54 (23%) seniors participants with a mean age (69.074 ± 6.810). There were highly statistically significant differences in the comparison between the participants’ ages (P 0.05). Conclusion: At the end of this study, we concluded that there are alterations in clinical and radiographic and clinical findings of stage IV periodontitis with the alteration of participants’ ages and nationality.