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OBJECTIVES: The purpose of this study was to generate radiographs including dentigerous cysts by applying the latest generative adversarial network (GAN; StyleGAN3) to panoramic radiography. METHODS: A total of 459 cystic lesions were selected, and 409 images were randomly assigned as training data and 50 images as test data. StyleGAN3 training was performed for 500 000 images. Fifty generated images were objectively evaluated by comparing them with 50 real images according to four metrics: Fréchet inception distance (FID), kernel inception distance (KID), precision and recall, and inception score (IS). A subjective evaluation of the generated images was performed by three specialists who compared them with the real images in a visual Turing test. RESULTS: The results of the metrics were as follows: FID, 199.28; KID, 0.14; precision, 0.0047; recall, 0.00; and IS, 2.48. The overall results of the visual Turing test were 82.3%. No significant difference was found in the human scoring of root resorption. CONCLUSIONS: The images generated by StyleGAN3 were of such high quality that specialists could not distinguish them from the real images.

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OBJECTIVE: Apical periodontitis (AP) is one of the most common pathologies of the oral cavity. An early and accurate diagnosis of AP lesions is crucial for proper management and planning of endodontic treatments. This study investigated the diagnostic accuracy of periapical radiography (PR) and panoramic radiography (PAN) in the detection of clinically/surgically/histopathologically confirmed AP lesions. METHOD: A systematic literature review was conducted in accordance with the PRISMA guidelines. The search strategy was limited to English language articles via PubMed, Embase and Web of Science databases up to June 30, 2023. Such articles provided diagnostic accuracy values of PR and/or PAN in the detection of AP lesions or alternatively data needed to calculate them. RESULTS: Twelve studies met inclusion criteria and were considered for the analysis. The average value of diagnostic accuracy in assessing AP lesions was 71% for PR and 66% for PAN. According to different accuracy for specific anatomical areas, it is recommended to use PR in the analysis of AP lesions located in the upper arch and lower incisor area, whereas lower premolar and molar areas may be investigated with the same accuracy with PR or PAN. CONCLUSIONS: Two-dimensional imaging must be considered the first-level examination for the diagnosis of AP lesions. PR had an overall slightly higher diagnostic accuracy than PAN. Evidence from this review provided a useful tool to support radiologists and dentists in their decision-making when inflammatory periapical bone lesions are suspected to achieve the best clinical outcome for patients, improving the quality of clinical practice.

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BACKGROUND: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. METHODS: The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. RESULTS: Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. CONCLUSIONS: In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options.

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The current study aimed to systematically review the literature on the accuracy of artificial intelligence (AI) models for osteoporosis (OP) diagnosis using dental images. A thorough literature search was executed in October 2022 and updated in November 2023 across multiple databases, including PubMed, Scopus, Web of Science, and Google Scholar. The research targeted studies using AI models for OP diagnosis from dental radiographs. The main outcomes were the sensitivity and specificity of AI models regarding OP diagnosis. The "meta" package from the R Foundation was selected for statistical analysis. A random-effects model, along with 95% confidence intervals, was utilized to estimate pooled values. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was employed for risk of bias and applicability assessment. Among 640 records, 22 studies were included in the qualitative analysis and 12 in the meta-analysis. The overall sensitivity for AI-assisted OP diagnosis was 0.85 (95% CI, 0.70-0.93), while the pooled specificity equaled 0.95 (95% CI, 0.91-0.97). Conventional algorithms led to a pooled sensitivity of 0.82 (95% CI, 0.57-0.94) and a pooled specificity of 0.96 (95% CI, 0.93-0.97). Deep convolutional neural networks exhibited a pooled sensitivity of 0.87 (95% CI, 0.68-0.95) and a pooled specificity of 0.92 (95% CI, 0.83-0.96). This systematic review corroborates the accuracy of AI in OP diagnosis using dental images. Future research should expand sample sizes in test and training datasets and standardize imaging techniques to establish the reliability of AI-assisted methods in OP diagnosis through dental images.

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OBJECTIVES: To determine the genetic effects of panoramic radiography on the epithelial cells of the buccal mucosa by examining the micronucleus formation in these cells. MATERIALS AND METHODS: In this cross-sectional study, exfoliative cytology samples were prepared from the buccal mucosa of 36 patients immediately before and 10 days after panoramic radiography. The samples were prepared using liquid-based cytology with Papanicolaou staining. The slides were simultaneously evaluated by two expert pathologists and the ratio of the number of cells with micronuclei to the total number of cells on the slide was reported as a percentage. Data analysis was done using paired-samples T test, Pearson's correlation coefficient, and covariance analysis (α = 0.05). RESULTS: The study sample consisted of 24 (66.67%) males and 12 females (33.33%) with a mean (SD) age of 27.36 (8.19) years. The frequency of cells with micronucleus before and after panoramic radiography was not statistically different (p = 0.468). Additionally, the frequency of micronucleated cells was not correlated with age (p = 0.737) and sex (p = 0.211). CONCLUSION: Panoramic exposure slightly increased the frequency of cells with micronucleus in epithelial cells of the buccal mucosa. However, this increase was not statistically significant.

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BACKGROUND: We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography. METHODS: Panoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0. The cracked tooth diagnosis model underwent fivefold cross-validation with 418 data instances divided into training, validation, and test sets at a ratio of 3:1:1. RESULTS: To evaluate the feasibility, the sensitivity, specificity, accuracy, and F1 score of the deep learning models were calculated, with values of 90.43-94.26%, 52.63-60.77%, 72.01-75.84%, and 76.36-79.00%, respectively. CONCLUSION: We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.

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Objective: This systematic review aims to summarize the evidence on the use and applicability of AI in impacted mandibular third molars. Methods: Searches were performed in the following databases: PubMed, Scopus, and Google Scholar. The study protocol is registered at the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY202460081). The retrieved articles were subjected to an exhaustive review based on the inclusion and exclusion criteria for the study. Articles on the use of AI for diagnosis, treatment, and treatment planning in patients with impacted mandibular third molars were included. Results: Twenty-one articles were selected and evaluated using the Scottish Intercollegiate Guidelines Network (SIGN) evidence quality scale. Most of the analyzed studies dealt with using AI to determine the relationship between the mandibular canal and the impacted mandibular third molar. The average quality of the articles included in this review was 2+, which indicated that the level of evidence, according to the SIGN protocol, was B. Conclusions: Compared to human observers, AI models have demonstrated decent performance in determining the morphology, anatomy, and relationship of the impaction with the inferior alveolar nerve canal. However, the prediction of eruptions and future horizons of AI models are still in the early developmental stages. Additional studies estimating the eruption in mixed and permanent dentition are warranted to establish a comprehensive model for identifying, diagnosing, and predicting third molar eruptions and determining the treatment outcomes in the case of impacted teeth. This will help clinicians make better decisions and achieve better treatment outcomes.

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BACKGROUND: It is still unclear whether the trabecular structure of the jaw is different in individuals with hypodontia than in those without hypodontia; this is important for clinicians. The aim was to determine whether the mandibular trabecular bone structure of children and adolescents with hypodontia differs from the control group by using the fractal analysis (FA) method in this study. METHODS: A total of 138 panoramic radiographs of 69 cases and 69 control subjects (mean age 13.2 ± 10.1) were evaluated. The age and gender of subjects in the case and control groups were matched. Three regions of interest (ROIs) were selected from the panoramic radiographs. ROI1 refers to the center of the ramus rising above the mandibular foramen. ROI2 refers to the area between the apical level of the mandibular molar and the upper border of the mandibular canal. ROI3, the missing tooth region, refers to the apical third of the mesial side of the erupting or fully erupted permanent mandibular first molar. Mann-Whitney U and Wilcoxon tests were used. p < 0.05 was accepted for the significance value. RESULTS: The mean fractal dimension (FD) values of ROI1, ROI2, and ROI3 were 1,25, 1,20, and 1,13, respectively. The means FD values obtained from the ramus region were higher than the other regions (p < 0.05). The FD values did not differ significantly according to gender and age (p > 0.05). The FD values of the case group were lower than the control group for ROI3 (p < 0.05). CONCLUSION: The results of this study showed that the mandibular trabecular bone quality of pediatric patients with one missing tooth was different from the healthy group. The difference in the mean FD values from the ROIs indicates that the ramus has a denser structure than the mandibular corpus. Clinicians should factor this into their dental treatment planning process.

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Osteoporosis is a common systemic bone disorder in the elderly, characterized by low bone mineral density and deterioration of bone structure. Apical periodontitis is an inflammatory response to the microbial infection of root canals, typically characterized by apical bone destruction surrounding the tooth's apex. This systematic review aimed to determine if osteoporosis affects the prevalence of apical periodontitis in adults. PRISMA guidelines have been followed. It included randomized clinical trials, cross-sectional, cohort, and case-control studies, and excluded non-relevant investigations and various secondary sources. A comprehensive search was performed in PubMed, Scopus, and Web of Science, until 13 March 2024. The Newcastle-Ottawa Scale was used to assess the quality of the three selected studies: two cross-sectional studies and one case-control study. One investigation only included post-menopausal women recruited at a dental university clinic, the other integrated data from the total hospital patients' population, and the third selected patients referred to the university dental clinic from the university hospital. The findings varied: one study noted a marginal association between low bone mineral density and apical periodontitis, another found a significant association, and the third, with the lowest risk of bias, reported no link. The main limitations were the scarcity of eligible studies and their overall quality. The review was registered in the PROSPERO database (CRD42024523705), applied strict inclusion criteria and thorough searches by experienced and independent reviewers. There is no strong evidence that adult individuals with osteoporosis have a higher probability of developing apical periodontitis. However, clinicians should remain cautious of osteoporosis's potential impact on apical periodontitis development.

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Statement of the Problem: As a developmental disorder characterized by an abnormal bend and angle in the longitudinal axis of the tooth root, dilaceration can cause complications in routine dental procedures such as endodontics, orthodontics, and surgical treatments. Purpose: The purpose of this study was to investigate the prevalence of dilaceration in maxillary and mandibular premolar teeth in a population of Shiraz city based on cone-beam computed tomography (CBCT). Materials and Method: This is a retrospective cross-sectional study on 927 premolar teeth and 132 CBCT radiographs of patients obtained from four private radiology clinics in Shiraz (Iran). In this study, the presence, location, direction, and severity of dilaceration in premolar roots as well as its relationship with gender were investigated. Chi-square and Fisher tests were used to analyze the data. Results: The results showed that 17% of the studied 927 teeth had dilaceration. The prevalence of dilaceration was significantly higher in women than in men (20.3% vs. 13.6%, p= 0.005). The dilaceration rates were significantly higher in the mandibular first and second premolar teeth (31.6% and 26%, p= 0.002) than in the other teeth. In addition, the highest prevalence was in the distal direction with mild severity in the apical third of the root (p< 0.001). Conclusion: According to the results of this study, the prevalence of dilaceration was relatively high in mandibular premolar teeth especially in women.

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OBJECTIVES: The study aimed to examine the authenticity of the often-mentioned statement that the third molar is the most frequently extracted tooth. This finding has not been shown previously in a large population-based sample. MATERIALS AND METHODS: Data comprised a nationally representative sample of 6082 panoramic radiographs taken from adults in the cross-sectional Health 2000 Survey. From the radiographs, all missing teeth were recorded. Information on congenital agenesis of individual teeth was retrieved from two published meta-analyses. Primary outcome was the frequency of missing teeth by tooth type. Explanatory variables were age, sex, and the jaw (maxilla/mandible). Statistical analyses included χ2 test and binomial logistic regression. RESULTS: Mean age of participants (46% men, 54% women) was 53 years (SD 14.6; range 30‒97 years). Missing teeth occurred more often in women than in men (P < 0.001). The third molar was most frequently missing and the canine least frequently. In the maxilla and mandible, the third molar was missing more often than each of the other tooth types up to the age of 80 years (P < 0.01). CONCLUSIONS: When considering the rates of congenital agenesis of individual teeth, it is concluded that the third molar remained the most common tooth extracted up till the age of 80 years. CLINICAL RELEVANCE: The third molar is the most common target for extraction, but also the most common tooth associated with malpractice claims, and therefore, calls for skills, adequate equipment, and other resources for a successful extraction.

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OBJECTIVE: To compare and analyze professional (P chart) and simple (S chart) clinical image evaluation charts for evaluating panoramic radiograph image quality. METHODS: Ten evaluators assessed 285 clinical panoramic radiograph images. The evaluators were divided into oral and maxillofacial radiologists (OMFR, n = 5) and general dentist (dentists not specializing in oral and maxillofacial radiology, G, n = 5) groups. For image evaluation, P and S charts provided by the Korean Academy of Oral and Maxillofacial Radiology were used. Scores of items for each evaluation chart were used to compare the reliability, correlation, evaluation scores, evaluation time, and preference, and statistical analyses were performed using IBM SPSS Statistics. RESULTS: The S chart showed similar levels of evaluation scores at shorter evaluation time, as compared to the P chart. In the results for each evaluation chart, all analyzed correlations were statistically significant. Total score, image density/contrast/sharpness, and overall image quality items showed a very high positive correlation in the P chart. While the overall range of correlation coefficients was relatively lower in the S chart than the P chart, the same items showed high correlation coefficients. In the preference evaluation, both the professional and generalist groups preferred the S chart. CONCLUSIONS: A comparative analysis with the P chart, revisions, and upgrades are needed for the S chart items that showed low correlations in this study, such as artifacts, coverage area, and patient movement.

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BACKGROUND: The radiation released at the time of dental panoramic radiographs causes genotoxic and cytotoxic effects on epithelial cells. OBJECTIVE: This research aimed to evaluate the changes in the frequencies of micronucleated cells in patients' buccal epithelial cells following dental panoramic radiography. METHODS: 74 patients were recruited for the study who were advised for panoramic radiographs. Using a wooden spatula, the buccal epithelial cells were scraped from both cheeks before to panoramic radiation exposure and ten days after the panoramic radiation exposure. Giemsa stain was used to stain the cells, and 500 cells were scored on a slide to determine the frequency of micronuclei. To determine the difference between the frequency of micronuclei before and after radiation exposure, a paired t-test was used in the statistical analysis. RESULTS: The proportion of micronuclei cells was 0.11% before radiation exposure and 0.57% following radiation exposure after 10 days. A statistically significant increase in the frequencies of micronuclei was noted after radiation exposure values. CONCLUSION: This study revealed the genotoxicity of epithelial cells with dental panoramic radiation exposure. It is advised to reduce the use of such radiographs and to use only when there is no other diagnostic tool that is helpful or when absolutely essential.

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The aim of this study was to compare the level of bone mass in digital orthopantomograms in two populations (medieval and current) using two radiomorphometric indexes, and to correlate the mandibular bone mass value, in the medieval mandible population, with stable isotope data δ13C and δ15N. An observational, cross-sectional, and analytical study on mandibles from two diachronic groups, 15 mandibles from the medieval settlement of La Torrecilla (Granada, Spain) and 15 mandibles from current patients at the Faculty of Dentistry of the University of Granada (Spain), matched by age and sex was conducted. The bone mass density was determined using the Mandibular Cortical Width Index (MCW) and the Mandibular Panoramic Index (PMI) in digital panoramic radiographs. In the medieval group, the values of bone mass density were correlated with those of two stable isotopes (δ13C and δ15N). The mean value of MCW in mm in the medieval group was 3.96 ± 0.60 (mean ± standard deviation) and in the current group was 4.02 ± 1.01. The PMI was 0.33 ± 0.06 and 0.35 ± 0.08 in the medieval and current groups respectively, with similar results in both groups (p = 0.820 and p = 0.575). A negative correlation was found between both morphometric indices and the δ15N isotope (rs = 0.56, p = 0.030 and rs = 0.61, p = 0.016, respectively). The bone mass density in mandibles belonging to the two compared populations, determined by two quantitative radiomorphometric indices, is similar. Within the medieval population, there is an inverse correlation between the δ15N value and bone mass density.

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The chronological age estimation of living individuals is a crucial part of forensic practice and clinical practice, such as in orthodontic treatment. It is well-known that methods for age estimation in living children should be tested on different populations. Ethnic affiliations in Brazil are divided into several major groups depending on the region, with the south of Brazil being known for its German immigration. (1) Background: This study aimed to evaluate the correlation between chronological age and dental age using Demirjian's method and Cameriere's method in a group of children from Joinville, South Brazil to investigate if both methods can be used to estimate dental age in this population. (2) Methods: The sample consisted of 229 panoramic radiographs (119 were males and were 110 females) from Brazilian children (ages ranging from 6 to 12 years). The chronological age at the time of the panoramic radiographic exam was calculated for each child. The dental age was estimated according to Demirjian's method and Cameriere's method. All continuous data were tested for normality by using the Shapiro-Wilk test. The Pearson correlation coefficient test was applied. An alpha of 5% (p < 0.05) was used for all analyses. (3) Results: The mean chronological age was 8.75 years. According to Demirjian's method, the mean dental age was 9.3 years, while according to Cameriere's method, the mean dental age was 8.66 years. A strong correlation between chronological age and dental age according to Demirjian (r = 0.776 and p < 0.0001) and Cameriere (r = 0.735 and p < 0.0001) was observed for both genders. (4) Conclusions: Both methods presented a good correlation with chronological age in the studied population and could be used to assess dental age in this population.

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OBJECTIVES: The purpose of this study was to develop a deep learning algorithm capable of diagnosing radicular cysts in the lower jaw on panoramic radiographs. MATERIALS AND METHODS: In this study, we conducted a comprehensive analysis of 138 radicular cysts and 100 normal panoramic radiographs collected from 2013 to 2023 at Clinical Hospital Dubrava. The images were annotated by a team comprising a radiologist and a maxillofacial surgeon, utilizing the GNU Image Manipulation Program. Furthermore, the dataset was enriched through the application of various augmentation techniques to improve its robustness. The evaluation of the algorithm's performance and a deep dive into its mechanics were achieved using performance metrics and EigenCAM maps. RESULTS: In the task of diagnosing radicular cysts, the initial algorithm performance-without the use of augmentation techniques-yielded the following scores: precision at 85.8%, recall at 66.7%, mean average precision (mAP)@50 threshold at 70.9%, and mAP@50-95 thresholds at 60.2%. The introduction of image augmentation techniques led to the precision of 74%, recall of 77.8%, mAP@50 threshold to 89.6%, and mAP@50-95 thresholds of 71.7, respectively. Also, the precision and recall were transformed into F1 scores to provide a balanced evaluation of model performance. The weighted function of these metrics determined the overall efficacy of our models. In our evaluation, non-augmented data achieved F1 scores of 0.750, while augmented data achieved slightly higher scores of 0.758. CONCLUSION: Our study underscores the pivotal role that deep learning is poised to play in the future of oral and maxillofacial radiology. Furthermore, the algorithm developed through this research demonstrates a capability to diagnose radicular cysts accurately, heralding a significant advancement in the field.

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Background: The application of artificial intelligence (AI) is gaining popularity in modern dentistry. AI has been successfully used to interpret dental panoramic radiographs (DPRs) and quickly screen large groups of patients. This cross-sectional study aimed to perform a population-based assessment of the oral health status and treatment needs of the residents of Kielce, Poland, and the surrounding area based on DPR analysis performed by a high-accuracy AI algorithm trained with over 250,000 radiographs. Methods: This study included adults who had a panoramic radiograph performed, regardless of indications. The following diagnoses were used for analysis: (1) dental caries, (2) missing tooth, (3) dental filling, (4) root canal filling, (5) endodontic lesion, (6) implant, (7) implant abutment crown, (8) pontic crown, (9) dental abutment crown, and (10) sound tooth. The study sample included 980 subjects. Results: The patients had an average of 15 sound teeth, with the domination of the lower dental arch over the upper one. The most commonly identified pathology was dental caries, which affected 99% of participants. A total of 67% of patients underwent root canal treatment. Every fifth endodontically treated tooth presented a periapical lesion. Of study group members, 82% lost at least one tooth. Pontics were identified more often (9%) than implants (2%) in replacing missing teeth. Conclusions: DPR assessment by AI has proven to be an efficient method for population analysis. Despite recent improvements in the oral health status of Polish residents, its level is still unsatisfactory and suggests the need to improve oral health. However, due to some limitations of this study, the results should be interpreted with caution.

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Background: The stage of tooth formation is one of the most reliable indicators for predicting a patient's developmental age by radiographs. This study compared the accuracy of three distinct dental age estimation methods (Demirjian, Nolla, and Willems) in children aged 3-17 in the northern Iranian population. Methods: This cross-sectional study examined panoramic radiographs of 434 children aged 3-17 from Mazandaran Province, Iran, who had teeth 31-37 present on the left mandible. This study employed the Demirjian, Nolla, and Willems methods to estimate the dental age of the sample and compare it with the chronological age. The data were analyzed using SPSS v16. A paired t-test was used to compare chronological and dental ages. The Pearson correlation was used to correlate the chronological and dental ages. The errors of different methods were compared using the Wilcoxon test. P values < 0.05 were considered significant for all tests except Wilcoxon. For Wilcoxon, a P value < 0.017 was considered significant. Results: The three methods presented differing mean estimated ages. The Demirjian method delivered the highest mean, and all three methods differed significantly when compared in pairs. The results showed that the Demirjian method overestimated chronological age by 0.25 years (P < 0.001) in girls and 0.09 years (P = 0.28) in boys. The Willems method underestimated chronological age by 0.05 years (P = 0.47) in girls and 0.12 years (P = 0.13) in boys. The Nolla method underestimated chronological age by 0.41 years (P < 0.001) in girls and 0.40 years (P < 0.001) in boys. The accuracy of each method varied with the patient's age. Conclusion: According to the findings, the Willems method outperformed the Demirjian method, and the Demirjian method exceeded the Nolla method for estimating dental age in Iranian children aged 3-17. Overall, the Demirjian method overestimated the age of the study population, whereas the other two underestimated it.

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Transfer learning (TL) is an alternative approach to the full training of deep learning (DL) models from scratch and can transfer knowledge gained from large-scale data to solve different problems. ImageNet, which is a publicly available large-scale dataset, is a commonly used dataset for TL-based image analysis; many studies have applied pre-trained models from ImageNet to clinical prediction tasks and have reported promising results. However, some have questioned the effectiveness of using ImageNet, which consists solely of natural images, for medical image analysis. The aim of this study was to evaluate whether pre-trained models using RadImageNet, which is a large-scale medical image dataset, could achieve superior performance in classification tasks in dental imaging modalities compared with ImageNet pre-trained models. To evaluate the classification performance of RadImageNet and ImageNet pre-trained models for TL, two dental imaging datasets were used. The tasks were (1) classifying the presence or absence of supernumerary teeth from a dataset of panoramic radiographs and (2) classifying sex from a dataset of lateral cephalometric radiographs. Performance was evaluated by comparing the area under the curve (AUC). On the panoramic radiograph dataset, the RadImageNet models gave average AUCs of 0.68 ± 0.15 (p < 0.01), and the ImageNet models had values of 0.74 ± 0.19. In contrast, on the lateral cephalometric dataset, the RadImageNet models demonstrated average AUCs of 0.76 ± 0.09, and the ImageNet models achieved values of 0.75 ± 0.17. The difference in performance between RadImageNet and ImageNet models in TL depends on the dental image dataset used.

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OBJECTIVE: The present study aimed to assess the consistencies and performances of deep learning (DL) models in the diagnosis of condylar osteoarthritis (OA) among patients with dentofacial deformities using panoramic temporomandibular joint (TMJ) projection images. METHODS: A total of 68 TMJs with or without condylar OA in dentofacial deformity patients were tested to verify the consistencies and performances of DL models created using 252 TMJs with or without OA in TMJ disorder and dentofacial deformity patients; these models were used to diagnose OA on conventional panoramic (Con-Pa) images and open (Open-TMJ) and closed (Closed-TMJ) mouth TMJ projection images. The GoogLeNet and VGG-16 networks were used to create the DL models. For comparison, two dental residents with < 1 year of experience interpreting radiographs evaluated the same condyle data that had been used to test the DL models. RESULTS: On Open-TMJ images, the DL models showed moderate to very good consistency, whereas the residents' demonstrated fair consistency on all images. The areas under the curve (AUCs) of both DL models on Con-Pa (0.84 for GoogLeNet and 0.75 for VGG-16) and Open-TMJ images (0.89 for both models) were significantly higher than the residents' AUCs (p < 0.01). The AUCs of the DL models on Open-TMJ images (0.89 for both models) were higher than the AUCs on Closed-TMJ images (0.72 for both models). CONCLUSIONS: The DL models created in this study could help residents to interpret Con-Pa and Open-TMJ images in the diagnosis of condylar OA.

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