OBJECTIVE: This study aims to investigate the incidence and clinical characteristics of concomitant hypodontia and hyperdontia (CHH) by performing panoramic radiographs.
METHODS: A total of 41 648 panoramic radiographs of pediatric patients who were admitted to the hospitals from January 2019 to May 2021 were reviewed, and 145 CHH patients were included in the study. The presence of CHH was recorded. SPSS 24.0 software was used for statistical analysis.
RESULTS: The prevalence of CHH was 0.35% (145/41 648). Males (102 cases) were obviously more than females (43 cases), and the difference between genders was statistically significant (P<0.001). The features of congenital permanent tooth loss in this group were predominantly 1 and 2 teeth missing and preferably mandibular lateral incisors and mandibular second premolars missing. The incidence of congenital permanent teeth loss was higher in the mandible than in the maxilla (P<0.001), but no difference was found in the distribution between left and right (P=0.84). The features of supernumerary teeth in this group were 1 and 2 teeth, mostly in the maxillary anterior area, mostly conical, mostly vertical inversion and orthotopic growth.
CONCLUSIONS: CHH is a rare mixed numeric dental anomaly characterized by congenital missing teeth and supernumerary teeth occurring in the same individual. CHH cases are higher in men than in women. The characteristics of their hypodontia and hyperdontia are similar to those of patients with congenital permanent tooth absence or supernumerary teeth. Early diagnosis of the condition and a multidisciplinary approach for management of such case is recommended.
目的: 应用曲面体层技术探讨少牙多牙症（CHH）的发生率和临床特征。方法: 收集2019年1月—2021年5月就诊的41 648例儿童口腔科患者的曲面体层片，纳入CHH患者145例，观察记录CHH的发生情况。应用SPSS 24.0软件统计分析所得的数据。结果: CHH的发生率为0.35%（145/41 648），男性（102例）多于女性（43例），性别间差异有统计学意义（P<0.001）。恒牙先天缺失特征：缺失1~2颗为主；最好发下颌侧切牙和下颌第二前磨牙；下颌恒牙先天缺失多于上颌恒牙先天缺失，二者差异有统计学意义（P<0.001）；左侧先天缺失与右侧先天缺失差异无统计学意义（P=0.84）。多生牙特征：数目1~2颗；多见于上颌前牙区；多为圆锥形；垂直倒置生长和垂直正位生长为主。结论: CHH是一种少见的混合牙齿数目异常，男性多于女性，恒牙先天缺失和多生牙的特征与单独发生的恒牙先天缺失/多生牙的特征相似，建议早期诊断和多学科治疗。.

This study developed and validated a deep learning-based method to automatically segment and number teeth in panoramic radiographs across primary, mixed, and permanent dentitions.
A total of 6,046 panoramic radiographs were collected and annotated. The dataset encompassed primary, mixed and permanent dentitions and dental abnormalities such as tooth number anomalies, dental diseases, dental prostheses, and orthodontic appliances. A deep learning-based algorithm consisting of a U-Net-based region of interest extraction model, a Hybrid Task Cascade-based teeth segmentation and numbering model, and a post-processing procedure was trained on 4,232 images, validated on 605 images, and tested on 1,209 images. Precision, recall and Intersection-over-Union (IoU) were used to evaluate its performance.
The deep learning-based teeth identification algorithm achieved good performance on panoramic radiographs, with precision and recall for teeth segmentation and numbering exceeding 97%, and the IoU between predictions and ground truths reaching 92%. It generalized well across all three dentition stages and complex real-world cases.
By utilizing a two-stage training framework with a large-scale heterogeneous dataset, the automatic teeth identification algorithm achieved a performance level comparable to that of dental experts.
Deep learning can be leveraged to aid clinical interpretation of panoramic radiographs across primary, mixed, and permanent dentitions, even in the presence of real-world complexities. This robust teeth identification algorithm could contribute to the future development of more advanced, diagnosis- or treatment-oriented dental automation systems.

Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple dental diseases on PRs, and to initially evaluate its performance.
The AI framework was developed based on 2 deep convolutional neural networks (CNNs), BDU-Net and nnU-Net. 1996 PRs were used for training. Diagnostic evaluation was performed on a separate evaluation dataset including 282 PRs. Sensitivity, specificity, Youden\'s index, the area under the curve (AUC), and diagnostic time were calculated. Dentists with 3 different levels of seniority (H: high, M: medium, L: low) diagnosed the same evaluation dataset independently. Mann-Whitney U test and Delong test were conducted for statistical analysis (ɑ=0.05).
Sensitivity, specificity, and Youden\'s index of the framework for diagnosing 5 diseases were 0.964, 0.996, 0.960 (impacted teeth), 0.953, 0.998, 0.951 (full crowns), 0.871, 0.999, 0.870 (residual roots), 0.885, 0.994, 0.879 (missing teeth), and 0.554, 0.990, 0.544 (caries), respectively. AUC of the framework for the diseases were 0.980 (95%CI: 0.976-0.983, impacted teeth), 0.975 (95%CI: 0.972-0.978, full crowns), and 0.935 (95%CI: 0.929-0.940, residual roots), 0.939 (95%CI: 0.934-0.944, missing teeth), and 0.772 (95%CI: 0.764-0.781, caries), respectively. AUC of the AI framework was comparable to that of all dentists in diagnosing residual roots (p > 0.05), and its AUC values were similar to (p > 0.05) or better than (p < 0.05) that of M-level dentists for diagnosing 5 diseases. But AUC of the framework was statistically lower than some of H-level dentists for diagnosing impacted teeth, missing teeth, and caries (p < 0.05). The mean diagnostic time of the framework was significantly shorter than that of all dentists (p < 0.001).
The AI framework based on BDU-Net and nnU-Net demonstrated high specificity on diagnosing impacted teeth, full crowns, missing teeth, residual roots, and caries with high efficiency. The clinical feasibility of AI framework was preliminary verified since its performance was similar to or even better than the dentists with 3-10 years of experience. However, the AI framework for caries diagnosis should be improved.

Objective To evaluate the accuracy of different convolutional neural networks (CNN),representative deep learning models,in the differential diagnosis of ameloblastoma and odontogenic keratocyst,and subsequently compare the diagnosis results between models and oral radiologists. Methods A total of 1000 digital panoramic radiographs were retrospectively collected from the patients with ameloblastoma (500 radiographs) or odontogenic keratocyst (500 radiographs) in the Department of Oral and Maxillofacial Radiology,Peking University School of Stomatology.Eight CNN including ResNet (18,50,101),VGG (16,19),and EfficientNet (b1,b3,b5) were selected to distinguish ameloblastoma from odontogenic keratocyst.Transfer learning was employed to train 800 panoramic radiographs in the training set through 5-fold cross validation,and 200 panoramic radiographs in the test set were used for differential diagnosis.Chi square test was performed for comparing the performance among different CNN.Furthermore,7 oral radiologists (including 2 seniors and 5 juniors) made a diagnosis on the 200 panoramic radiographs in the test set,and the diagnosis results were compared between CNN and oral radiologists. Results The eight neural network models showed the diagnostic accuracy ranging from 82.50% to 87.50%,of which EfficientNet b1 had the highest accuracy of 87.50%.There was no significant difference in the diagnostic accuracy among the CNN models (P=0.998,P=0.905).The average diagnostic accuracy of oral radiologists was (70.30±5.48)%,and there was no statistical difference in the accuracy between senior and junior oral radiologists (P=0.883).The diagnostic accuracy of CNN models was higher than that of oral radiologists (P<0.001). Conclusion Deep learning CNN can realize accurate differential diagnosis between ameloblastoma and odontogenic keratocyst with panoramic radiographs,with higher diagnostic accuracy than oral radiologists.

OBJECTIVE: This study aimed to investigate the clinical characteristics of congenital deciduous teeth absence and its permanent teeth performance type by using panoramic radiographs.
METHODS: A total of 15 749 panora-mic radiographs of 3-6-year-old children with deciduous dentition were collected from January 2020 to December 2021. The incidence of congenital deciduous teeth absence was observed, and the abnormality of permanent teeth was recor-ded. SPSS 24.0 software was used for statistical analysis.
RESULTS: The incidence of congenital deciduous teeth absence was 2.54% (400/15 749), which was found in 217 girls and 183 boys, and the difference between the genders was statistically significant (P=0.003). The absence of one and two deciduous teeth accounted for 99.75% (399/400) of the subjects. In addition, 92.63% (490/529) of mandibular deciduous lateral incisor was congenitally absent, 44.80% (237/529) of deciduous teeth was absent in the left jaw, and less than 55.20% (292/529) was absent in the right; the difference between them was statistically significant (P=0.017). The absence of 96.41% (510/529) deciduous teeth in the mandibular was significantly more than that of 3.59% (19/529) in the maxillary, and the difference between was statistically significant (P=0.000). Furthermore, 68.00% (272/400) and 32.00% (128/400) of deciduous teeth were absent in unilateral and bilateral, respectively, and the difference was statistically significant (P=0.000). Four types of congenital deciduous teeth absence with permanent teeth were observed as follows: 1) 73.91% (391/529) of permanent teeth was absent; 2) 20.60% (109/529) of permanent teeth was not absent; 3) the number of fused permanent teeth accounted for 4.91% (26/529); 4) the number of supernumerary teeth was 0.57% (3/529).
CONCLUSIONS: Although the absence of congenital deciduous teeth is less common than that of permanent teeth, it affects deciduous and permanent teeth to some extent. Dentists should pay attention to trace and observe whether abnormalities are present in the permanent teeth and take timely measures to maintain children\'s oral health.
目的: 应用曲面断层技术研究乳牙先天缺失的特点及其继承恒牙的表现类型。方法: 收集2020年1月—2021年12月就诊的3~6岁乳牙列期儿童的曲面体层片，共纳入15 749张，观察记录乳牙先天缺失的发生情况，同时记录其继承恒牙是否存在异常。应用SPSS 24.0统计软件分析所得的数据。结果: 乳牙先天缺失的发生率为2.54%（400/15 749），女性217例多于男性183例，性别间差异有统计学意义（P=0.003）；乳牙先天缺失1~2颗例数占99.75%（399/400）；下颌乳侧切牙先天缺失颗数占92.63%（490/529）；左侧乳牙先天缺失颗数占44.80%（237/529），右侧乳牙先天缺失颗数占55.20%（292/529），两者差异有统计学意义（P=0.017）。上颌乳牙先天缺失颗数占3.59%（19/529），下颌乳牙先天缺失颗数占96.41%（510/529），两者差异有统计学意义（P=0.000）；单侧乳牙缺失占68.00%（272/400），双侧乳牙缺失占32.00%（128/400），两者差异有统计学意义（P=0.000）。乳牙先天缺失其继承恒牙有4种表型：1）继承恒牙缺失颗数占73.91%（391/529）；2）继承恒牙不缺失颗数占20.60%（109/529）；3）继承恒牙融合牙颗数占4.91%（26/529）；4）继承恒牙多生牙颗数占0.57%（3/529）。结论: 虽然乳牙先天缺失较恒牙先天缺失少见，但是乳牙先天缺失在一定程度上影响到乳牙列和恒牙列，口腔医生应注意追踪观察恒牙列是否存在异常，及时采取措施，维护儿童口腔健康。.

Background: It is known that oral diseases such as periodontal (gum) disease are closely linked to various systemic diseases and disorders. Deep learning advances have the potential to make major contributions to healthcare, particularly in the domains that rely on medical imaging. Incorporating non-imaging information based on clinical and laboratory data may allow clinicians to make more comprehensive and accurate decisions. Methods: Here, we developed a multimodal deep learning method to predict systemic diseases and disorders from oral health conditions. A dual-loss autoencoder was used in the first phase to extract periodontal disease-related features from 1188 panoramic radiographs. Then, in the second phase, we fused the image features with the demographic data and clinical information taken from electronic health records (EHR) to predict systemic diseases. We used receiver operation characteristics (ROC) and accuracy to evaluate our model. The model was further validated by an unseen test dataset. Findings: According to our findings, the top three most accurately predicted chapters, in order, are the Chapters III, VI and IX. The results indicated that the proposed model could predict systemic diseases belonging to Chapters III, VI and IX, with AUC values of 0.92 (95% CI, 0.90-94), 0.87 (95% CI, 0.84-89) and 0.78 (95% CI, 0.75-81), respectively. To assess the robustness of the models, we performed the evaluation on the unseen test dataset for these chapters and the results showed an accuracy of 0.88, 0.82 and 0.72 for Chapters III, VI and IX, respectively. Interpretation: The present study shows that the combination of panoramic radiograph and clinical oral features could be considered to train a fusion deep learning model for predicting systemic diseases and disorders.

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.

This study aimed to develop a novel detection model for automatically assessing the real contact relationship between mandibular third molars (MM3s) and the inferior alveolar nerve (IAN) based on panoramic radiographs processed with deep learning networks, minimizing pseudo-contact interference and reducing the frequency of cone beam computed tomography (CBCT) use. A deep-learning network approach based on YOLOv4, named as MM3-IANnet, was applied to oral panoramic radiographs for the first time. The relationship between MM3s and the IAN in CBCT was considered the real contact relationship. Accuracy metrics were calculated to evaluate and compare the performance of the MM3-IANnet, dentists and a cooperative approach with dentists and the MM3-IANnet. Our results showed that in comparison with detection by dentists (AP = 76.45%) or the MM3-IANnet (AP = 83.02%), the cooperative dentist-MM3-IANnet approach yielded the highest average precision (AP = 88.06%). In conclusion, the MM3-IANnet detection model is an encouraging artificial intelligence approach that might assist dentists in detecting the real contact relationship between MM3s and IANs based on panoramic radiographs.

Periodontitis is a prevalent and irreversible chronic inflammatory disease both in developed and developing countries, and affects about 20-50% of the global population. The tool for automatically diagnosing periodontitis is highly demanded to screen at-risk people for periodontitis and its early detection could prevent the onset of tooth loss, especially in local communities and health care settings with limited dental professionals. In the medical field, doctors need to understand and trust the decisions made by computational models and developing interpretable models is crucial for disease diagnosis. Based on these considerations, we propose an interpretable method called Deetal-Perio to predict the severity degree of periodontitis in dental panoramic radiographs. In our method, alveolar bone loss (ABL), the clinical hallmark for periodontitis diagnosis, could be interpreted as the key feature. To calculate ABL, we also propose a method for teeth numbering and segmentation. First, Deetal-Perio segments and indexes the individual tooth via Mask R-CNN combined with a novel calibration method. Next, Deetal-Perio segments the contour of the alveolar bone and calculates a ratio for individual tooth to represent ABL. Finally, Deetal-Perio predicts the severity degree of periodontitis given the ratios of all the teeth. The Macro F1-score and accuracy of the periodontitis prediction task in our method reach 0.894 and 0.896, respectively, on Suzhou data set, and 0.820 and 0.824, respectively on Zhongshan data set. The entire architecture could not only outperform state-of-the-art methods and show robustness on two data sets in both periodontitis prediction, and teeth numbering and segmentation tasks, but also be interpretable for doctors to understand the reason why Deetal-Perio works so well.

(1) Background: Medial sigmoid depression (MSD) of the mandibular ramus is an anatomical variation that resembles non-odontogenic cystic lesion. (2) Aim: The aim of this systematic review was to survey the literature to identify the relevant journal publications, reveal their scientific impact in terms of citations and compare the reported prevalence of MSD. (3) Materials and methods: PubMed, Google Scholar, Scopus and Web of Science were queried to identify relevant publications. The search string was: \"medial depression of mandibular ramus\" OR \"medial depression of the mandibular ramus\" OR \"medial depression of the mandibular rami\" OR \"medial depression of mandibular rami\" OR \"medial sigmoid depression\". (4) Results: Eight studies were identified. Dry mandibles and patient dental panoramic radiographs were evaluated in four and seven of the eight studies, respectively. The prevalence of MSD varied from 20.2% to 82.0%. In male and female patients, the prevalence was 18.3-76.0% and 22.0-64.0%, respectively. MSD tended to occur bilaterally and most prevalent in patients with Angle\'s Class II occlusion. The semilunar and triangular shapes were more common than teardrop and circular shapes. The most cited study had 12 citations. (5) Conclusions: MSD was a seldom investigated and cited anatomical variation that was not uncommon. Its recognition should be further promoted.