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Objective The aim of the present study was to compare radiomorphometric measurements on panoramic images of patients with hyperthyroidism (HT) to those of a control population. Study design In this retrospective study, the mental index (MI), inferior panoramic mandibular index (PMI), antegonial index (AI), gonial index (GI), and mandibular cortical index (MCI) were evaluated on dental panoramic radiographs (DPRs) of 40 patients with HT and 40 healthy age- and sex-matched individuals and compared between the groups. Quantitative data (MI, PMI, AI, and GI) were analyzed with the Student t and Mann-Whitney U tests. Categorical data (MCI) were analyzed with the chi-squared test. Results Statistically significant differences between the patient and control groups were found for inferior PMI (p = 0.001) and AI (p = 0.017) values, with smaller mean values among the patients. However, the measurements of MI (P = 0.59) and GI (P = 0.164) and the MCI distribution (P = 0.13) were similar in both groups. Conclusions Evaluating the radiomorphometric indices of inferior PMI and AI on the DPRs of patients with HT who are at high risk of secondary osteoporosis could be helpful in the early diagnosis of osteoporotic changes and in formulating surgical treatment plans.

Radiograph plays the major role of diagnosis, treatment and surgery in the Dental field. There are many types of Intra and extra oral radiographs in which Dental Panoramic Radiograph helps in visualising the full view of the oral cavity. Pulpitis is the dental diseases caused due to the inflammation of the dental pulp from untreated caries, trauma or multiple restorations which leads to Apical Periodontitis. To predict the severity of pulp vitality pulp inflammation has to be evaluated. Radiographs helps the dentist in diagnosing the extent of tooth decay and inflammation. An automatic diagnostic model is proposed using robust algorithms to diagnose pulpits. Dental Panoramic Radiograph is used in the proposed research to diagnose the pulpitis and to classify the normal teeth from the pulpitis. The collected images are pre-processed using Histogram Equalization and filtered using Gaussian and Median filters. Modified K-Means algorithm is applied to segment the bony and teeth area from the oral cavity area. Integral Histogram of Gradients with Discrete Wavelet Transform feature extraction techniques and Multi-Layer Neural Network Classifier is proposed to achieve the accuracy of 91.09% which can be used as an assistive tool by the dentist to diagnose pulpitis.

Purpose The aim of this study was to assess the suitability of the 8-tooth method on the Malaysian Malays aged 5.00–17.99 years originally introduced by Chaillet and Demirjian and to develop a more reliable teeth maturity scores for age estimation based on artificial neural networks (ANN). A total of 1569 dental panoramic tomographs of Malaysian Malays aged 5.00–17.99 years were assessed. Tooth development was initially recorded using a score chart adapted from Demirjian's eight developmental stages. Maturity scores were converted to dental age (DA). The mean and standard deviation of chronological age (CA), DA and the difference between CA and DA were calculated. Artificial neural networks was then employed to develop new dental maturity scores (NDA) and its reliability was assessed. Results Initial comparison of known CA with estimated DA derived from Chaillet and Demirjian's dental maturity scores showed that DA was consistently underestimated by 2.09 ± 0.90 years for Malay boys and 2.79 ± 0.99 years for Malay girls for all age groups (paired t-test, p 0.05). Conclusion In conclusion, the new dental maturity scores were highly accurate in estimating the age of Malaysian Malay children and adolescents.

Osteoporosis is one type of disease that is not easily detected. This disease can cause fractures for the sufferer. Early detection of osteoporosis is crucial to prevent fractures. This study aims to detect osteoporosis through features extracted from cortical bone and trabeculae in dental panoramic images. The results of the selected feature extraction are trained using an artificial neural network. Based on the study results, the dominant features for osteoporosis detection are radio morphometric index and morphological features. The accuracy, sensitivity, and specificity of the J48 and Learning Vector Quantization (LVQ) are 83.88 %, 78.57 %, and 100 %, respectively.

Objectives: This study aimed to develop a fully automated human identification method based on a convolutional neural network (CNN) with a large-scale dental panoramic radiograph (DPR) data set. Methods: In total, 2760 DPRs from 746 subjects who had 2–17 DPRs with various changes in image characteristics due to various dental treatments (tooth extraction, oral surgery, prosthetics, orthodontics, or tooth development) were collected. The test data set included the latest DPR of each subject (746 images) and the other DPRs (2014 images) were used for model training. A modified VGG16 model with two fully connected layers was applied for human identification. The proposed model was evaluated with rank-1, -3, and -5 accuracies, running time, and gradient-weighted class activation mapping (Grad-CAM)-applied images. Results: This model had rank-1, -3, and -5 accuracies of 82.84%, 89.14%, and 92.23%, respectively. All rank-1 accuracy values of the proposed model were above 80% regardless of changes in image characteristics. The average running time to train the proposed model was 60.9 s per epoch, and the prediction time for 746 test DPRs was short (3.2 s/image). The Grad-CAM technique verified that the model automatically identified humans by focusing on identifiable dental information. Conclusion: The proposed model showed good performance in fully automatic human identification despite differing image characteristics of DPRs acquired from the same patients. Our model is expected to assist in the fast and accurate identification by experts by comparing large amounts of images and proposing identification candidates at high speed.

The South African Bill of Rights and the Children's Act define a 'child' as a person under the age of 18 years. The age of 18 years is therefore significant for legal purposes in South Africa. The third molar is an important indicator in determining the 18-year threshold. Human biological growth markers are accepted indicators of a subject attaining the age of 18 years. A recent study by Roberts et al. suggested that the relative width of the distal root canals (RCW) of the lower left permanent molars (Fédération Dentaire Internationale notation 36, 37 and 38) as visualised on dental panoramic radiographs can be used as such a growth marker. This study aimed to validate this human biological growth marker in both black and white populations living in South Africa according to Roberts' method. The findings of this validation study were in agreement with this method which showed that individuals with Demirjian stage H left molars and category RCW-C were indeed over the 18-year threshold. Important aspects regarding the rationality and application of Roberts' method are discussed. The authors conclude that this method should not be used in isolation but as an adjunct with other age estimation methods.

Background/Aim: Panoramic radiographs have been used widely for pre-implant evaluation and the preparation of treatment protocols. Panoramic radiography is often the first choice method for the implant placements because it gives information on the overall shape of the jaws, the position of the maxillary sinus floor and the nasal cavity floor, and vertical position of the mandibular canal and the mental foramen in relation to dental implants. The specific aim of the present study was to evaluate 10-year survival rate of dental implants by analysis of dental panoramic radiographs. Material and Methods: This retrospective study was conducted on 507 panoramic radiographs of 156 implant patients. Initial oral health status, proceedings of Restorative/Endodontic treatments before implant placement, missing number of teeth and edentulism types, finalization of implant related prosthetic treatments during controls, implant quality scorings at baseline/ controls and implant fails were analyzed. Results: The study results reveal statistically significant difference between the performed R/E treatment rates, according to the need for R/E treatments (p= 0.008). During the initial radiograph 96.1% of the implants were successful and 3.9% had satisfactory survival. At last control 86% of the implants were successful, 10.3% had satisfactory survival, 1% had impairment in survival and 2.8% were unsuccessful. The change in the last control compared to the initial radiograph is statistically significant (p= 0.000) regarding satisfactory survival. Conclusions: Long-term implant survival is not significantly affected by gender, age, type of edentulism, the presence of periodontal disease, R/E treatment needs, implant location, prosthesis type, and the presence of multiple implants.

Amaç: Bu çalışmanın amacı yapay zeka (Artificial Intelligence) (AI) sistemleri kullanılarak dental panoramik radyografik görüntülerden alveoler kemik kaybını tespit etmektir.Gereç ve Yöntem: Bu çalışmada toplam 2276 panoramik radyografik görüntü kullanıldı. Bunların 1137'si kemik yıkımı olan vakalara aitken, 1139'u periodontal olarak sağlıklıydı. Veri kümesi eğitim (n = 1856), doğrulama (n = 210) ve test seti (n = 210) olarak üç bölüme ayrıldı. Veri setindeki tüm görüntüler eğitimden önce 1472x718 piksel olarak yeniden boyutlandırıldı. Açık kaynaklı python programlama dili ve OpenCV, NumPy, Pandas ve Matplotlib kütüphaneleri etkili bir şekilde kullanılarak rastgele bir dizi oluşturuldu. Ön işleme için önceden eğitilmiş bir Google Net Inception v3 CNN ağı kullanılmış ve veri setleri aktarım öğrenimi kullanılarak eğitildi. Tanısal performans, duyarlılık, özgüllük, kesinlik, doğruluk ve F1 skoru kullanılarak konfüzyon matrisi ile değerlendirildi.Bulgular: Kemik kaybı olan 105 olgunun 99'u AI sistemi ile tespit edildi. Duyarlılık 0.94, özgüllük 0.88, hassasiyet 0.89, doğruluk 0.91 ve F1 skoru 0.91 idi.Sonuç: Konvolüsyon nöral ağ modeli periodontal kemik kayıplarını belirlemede başarılıdır. Gelecekte tanı ve tedavi planlamasında hekimlerin çalışmasını kolaylaştıran bir sistem olarak kullanılabilir., Objectives: The aim of this study was to detect alveolar bone loss from dental panoramic radiographic images using artificial intelligence systems.Material and Methods: A total of 2276 panoramic radiographic images were used in this study. While 1137 of them belong to cases with bone destruction, 1139 were periodontally healthy. The dataset is divided into three parts as training (n=1856) , validation (n=210) and testing set (n= 210). All images in the data set were resized to 1472x718 pixels before training. A random sequence was created using the open-source python programming language and OpenCV, NumPy, Pandas, and Matplotlib libraries effectively. A pre-trained Google Net Inception v3 CNN network was used for preprocessing and data sets were trained using transfer learning. Diagnostic performance was evaluated with the confusion matrix using sensivitiy, specificity, precision, accuracy and F1 score.Results: Of the 105 cases with bone loss, 99 were detected by the AI system. Sensitivity was 0.94, specificity 0.88, precision 0.89, accuracy 0.91 and F1 score 0.91. Conclusion: The convolutional neural network model is successful in determining periodontal bone losses. It can be used as a system to facilitate the work of physicians in diagnosis and treatment planning in the future.

Computer-assisted analysis of dental radiograph in dentistry is getting increasing attention from the researchers in recent years. This is mainly because it can successfully reduce human-made error due to stress, fatigue or lack of experience. Furthermore, it reduces diagnosis time and thus, improves overall efficiency and accuracy of dental care system. An automatic teeth recognition model is proposed here using residual network-based faster R-CNN technique. The detection result obtained from faster R-CNN is further refined by using a candidate optimization technique that evaluates both positional relationship and confidence score of the candidates. It achieves 0.974 and 0.981 mAPs for ResNet-50 and ResNet-101, respectively with faster R-CNN technique. The optimization technique further improves the results i.e. F1 score improves from 0.978 to 0.982 for ResNet-101. These results verify the proposed method’s ability to recognize teeth with high degree of accuracy. To test the feasibility and robustness of the model, a tenfold cross validation (CV) is presented in this paper. The result of tenfold CV effectively verifies the robustness of the model as the average F1 score obtained is more than 0.970. Thus, the proposed model can be used as a useful and reliable tool to assist dental care professionals in dentistry.

Aim and objective To evaluate the possible genotoxic effect of X-rays on buccal mucosa while exposing to dental panoramic radiography using micronucleus test. Materials and methods The study group comprised of 30 healthy subjects, 15 males and 15 females, aged between 24 years and 65 years. Samples were obtained from the exfoliated oral mucosa cells of buccal mucosa before and 12 days after exposing the patients to panoramic radiography. Results The study reported that there was no significant increase in the number of micronuclei cells present before and after panoramic radiography. Positive correlation existed between age with pre- and postexposure micronuclei. Conclusion Diagnostic dental panoramic radiograph does not induce micronuclei in the target buccal epithelium cells. A positive correlation between age and micronuclei frequency was established. Clinical significance Panoramic radiographs does not induce cytotoxicity but increase frequency may be vulnerable to genotoxic effects in buccal mucosal cells. Hence, dental radiographs should be prescribed only when necessary. How to cite this article Santhosh K, Manzoor S, Sushanth A, et al. A Cross-sectional Study to Evaluate Nuclear Changes in Buccal Mucosa Following Panoramic Radiography. J Contemp Dent Pract 2020;21(11):1258–1261.

Aim. To compare the efficiency of removable mini-implant retained dentures using different types of attachment.Materials and methods. The study included 51 edentulous patients. The patients were divided into three groups according to the attachment type and abutment shape: 1) ball-and-socket attachment, ball abutment (n = 26); 2) locator attachment, equator abutment, XiVE LOCATOR system (n = 14); 3) bar attachment (n = 11). Removable dentures were made of ordinary acrylic plastic. The placement of intraosseous dental implants with their sublingual engraftment was performed according to the traditional two-stage delayed-load procedure. The efficiency of implants was evaluated using the method of M.Z. Mirgazizov and the eponymous index. The dynamics of vertical bone resorption around implants was determined using dental panoramic radiography (6 months, 1 and 2 years after the treatment). A periotest was performed 6 months after the end of the orthopaedic treatment.Results. The one-year survival rate of implants reached 96.2% in group 1, amounting to 100% in groups 2 and 3. The two-year survival rate came to 92.3% in group 1, reaching 92.9% and 100% in groups 2 and 3, respectively. Inflammatory phenomena characteristic of peri-implantitis were noted one year after the implant placement in 7.7% (n = 2) of patients in group 1 and in 7.1% of patients in group 2, with no inflammatory phenomena being observed in group 3. Two years after the implant placement, peri-implantitis was observed in 19.2% and 21.4% of patients in groups 1 and 2, respectively, coming to 9.1% in group 3. The periotest revealed that six months after the placement, implant stability was higher in group 3 for both the lower and upper jaw (in comparison with groups 1 and 2), as evidenced by the shift in periotest values towards the negative part of the scale.Conclusion. The study revealed that bar attachment, as well as locator attachment (equator abutments), of removable dentures supported by mini implants is more efficient as compared to the ball-and-socket attachment.

We investigated the development of third molars among Malaysians (including variations between jaws and genders) using Demirjian’s method. Dental panoramic radiographs of 1224 subjects aged 8 to 24 years were examined, and the molars were assigned Demirjian et al.’s development grades (A–H). Results indicated that 18.8% had congenitally missing or extracted third molars. Development of molars begins earlier in females (also in the mandible), but by age 9, male children's molar development speeds up with more advanced grades in their middle teens than females. Grade C indicates the subject is a juvenile, while initiation of root development (Grade E), was observed from 13 years on. Grade H can occur in a child aged 18 years who technically is still a juvenile. We compared the development and growth patterns of the third molar from both the maxilla and the mandible.

Calcification regions are sometimes observed on dental panoramic radiographs and it is known that these regions are a sign of vascular disease. It has been pointed out that the detection of calcification regions in dental panoramic radiographs can encourage patients to undergo medical checkups by a physician. Medical checkups can prevent the sudden onset of vascular disease. For this purpose, a method of automatically detecting calcification regions using an object detector based on deep learning has been proposed. Although this method significantly reduces the number of false positives compared with conventional methods based on image features, its detection accuracy is still insufficient. In this paper, we propose a method of detecting calcification regions in dental panoramic radiographs using a novel object detector based on deep learning. On the basis of PraNet, which has been increasingly applied to medical image processing in recent years, we introduce the Double U-Net structure and enhance the prediction accuracy for the initial guidance region. The proposed method can improve the detection accuracy for calcification regions. The experimental results show that the proposed method improves the detection performance compared with other methods.

The development of radiology, rapid prototyping techniques, and the increasingly common use of 3D printing in dentistry inspires the use of these techniques to improve diagnostic and therapeutic processes. This study aimed to conduct a retrospective comparative analysis of dental panoramic radiographs, cone-beam computed tomography, and 3D printing in preoperative assessment of the procedure’s difficulty. Thirty clinical cases with a high degree of difficulty were selected, and based on evaluation with CBCT, a virtual 3D model of the region of surgical procedure was created, which was then printed using a 3D printer. The comparative analysis included the linear measurements performed in dental panoramic radiographs, cone-beam computed tomography, and 3D models in a preoperative assessment of the degree of retention and difficulty of impacted mandibular third molars in the mandible. Linear measurements performed on dental panoramic radiographs were significantly lower than in cone-beam computed tomography and 3D models. No statistically significant differences were obtained in linear measurements between 3D models and cone-beam computed tomography images except for the measurement of the lingual lamina thickness, however, due to the insignificant differences in measurements, with a mean of only 80 µm, the elective procedure of removal of the impacted third molar in the mandible may be safe.

Bone age measurement is the process of evaluating the level of skeletal maturity to estimate actual age of bone. This evaluation is usually done by comparing a radiograph of a bone with an existing standard chart that includes a set of identifiable images at each stage of development. Manual methods are based on the analysis of specific areas of bone images or dental structures. Both methods are highly dependent to human experience and are time-consuming. An automated model therefore is needed to estimate the age accurately. In this study, we propose a hybrid convolutional neural network (CNN) combining K nearest neighbours (KNN) and PCA to estimate the age of bone automatically and accurately. We applied our model, HCNN-KNN, on a dataset collected by dental teaching institutes and private dental clinics in Malaysia. A total of 1,922 panoramic dental radiographs of dental patients aged between 15 to 25 years old were obtained from the various centres. These radiographs were separated by age, classified as those in the range of 12-months, six-months, three-months, and one-month gaps. This novel investigation, implemented for the first time with precision to the range of the age for ± twelve months, ± six months, ± three months, and ± one month, and these age ranges determine the age of minors which could help the model to find better features and train the model more accurately. Replacing SoftMax with KNN generally improves traditional CNN performance to reduce the noises in images. Therefore, the optimal number of image similarities in a larger dataset is more significant, and the proposed method can benefit from large amounts of annotated data. Since the similarities of radiographic images are very similar, there may be several similar possibilities in the SoftMax classification method. These similar probabilities increase the risk of misdiagnosis of bone age measurements. Therefore, replacing KNN with SoftMax is the best choice for age group differentiation in classifiers. Finally, the accuracy rate is evaluated with the accuracy criterion according to the equation in confusion metrics and comparing existing models. The accuracy results on the dataset by ± 12 months, ± 6-months, ± 3-months, and ± 1-month are 99.98, 99.96, 99.87, and 98.78, respectively.

The determination of age of majority (the 18-year-old threshold) using the popular Demirjian tooth staging method is unreliable, so other maturity markers are required. This study examines whether the Root Pulp Visibility (RPV) of the mandibular third molar is a useful indicator of age. One thousand six dental panoramic radiographs (DPTs) were examined and the left mandibular third molar assessed according to the RPV stages using the method of Olze et al. (Int J Legal Med 124:183–186, 2010) as modified by Lucas et al. (Forensic Sci Int 270:98–102, 2017). Six hundred sixty-two DPTs, 288 males and 374 females, satisfied the inclusion criteria. Individuals who had reached stages RPV-A and RPV-B were represented in a wide range of ages spanning the 18-year-old threshold. Individuals who had reached stages RPV-C and RPV-D were all above the 18-year-old threshold. It was not possible to analyse a large number of DPTs for various reasons. Individuals whose mandibular third molar exhibited stages RPV-C and RPV-D may be deemed to have reached the age of majority. These results are consistent with other published studies. Variation in the rate of development of the third molar limits the applicability of this method. RPV staging is an accurate method of determining the 18-year-old threshold. Substitute methods are required in a large number of cases.

All relevant ‘dental’ radiographic anomalies require a report, be they related to the teeth, mandible, maxillary sinuses, temporomandibular joints or soft tissues. However, non-dental incidental findings are often present on dental radiographs, some of which may be due to systemic disease: these should also be assessed. We compare the confidence at diagnosing, and subsequent management of, three such incidental findings between specialist dental and maxillofacial radiologists and recently qualified dentists. The imaging features and clinical implications of dense bone islands, carotid artery calcification and osteoporosis are described. CPD/Clinical Relevance: Dental panoramic radiographs cover structures not confined to the tooth-bearing areas and incidental findings in these regions should be recognized and appropriately reported, in line with IR(ME)R legislation.

The aim of this study was to find out if juxta-apical radiolucency (JAR) is a reliable risk factor for injury to the inferior alveolar nerve (IAN) during removal of lower third molars. We designed a cohort study of patients whose dental panoramic tomograms (DPT) had shown JAR before complete removal of lower wisdom teeth. The outcome variable was postoperative permanent neurosensory disturbance of the IAN. A total of 39 patients (50 lower third molars) were identified and screened for permanent neurosensory disturbance. None reported any permanently altered sensation 18 months after the operation. Based on our group, the presence of JAR does not seem to be a reliable predictor of the risk of permanent injury to the IAN during removal of lower third molars.

Dental panoramic radiography is a commonly employed investigation in dentistry, however, its use in the primary indication of maxillary sinus disease is often underestimated. Being able to identify anomalies or abnormalities involving the maxillary sinus on radiographs will facilitate early intervention and appropriate referral to the relevant specialties. CPD/Clinical Relevance: Dentists should have a good understanding of radiographic anatomy of the maxillary sinus. Recognizing radiographic changes allows appropriate referral and improved patient care.

Radiographs are X-rays of the craniofacial area, used for orthodontic diagnosis and treatment planning. Analysis of radiographs is a manual process. Public medical centers in developing countries such as South Africa experience a bottleneck in the analysis of these radiographs, due to excessive numbers of patients and severe shortages in orthodontic radiologists that serve at these public medical centers. Access to dental diagnostics is therefore becoming an ever-increasing problem in rural communities. This paper reports on the first phase of a framework to automate the analysis of panoramic radiographs, which are X-rays of the frontal croniofacial area. This first phase automates the process to predict whether a captured panoramic radiograph is workable or not, i.e. whether automated analysis of the X-ray can proceed. A is trained on a large set of panoramic radiographs, and results show that the prediction accuracy of this is very good in identifying workable radiographs.

Objectives The goal of this study was to develop and evaluate the performance of a new deep-learning (DL) artificial intelligence (AI) model for diagnostic charting in panoramic radiography. Methods One thousand eighty-four anonymous dental panoramic radiographs were labeled by two dento-maxillofacial radiologists for ten different dental situations: crown, pontic, root-canal treated tooth, implant, implant-supported crown, impacted tooth, residual root, filling, caries, and dental calculus. AI Model CranioCatch, developed in Eskisehir, Turkey and based on a deep CNN method, was proposed to be evaluated. A Faster R-CNN Inception v2 (COCO) model implemented with the TensorFlow library was used for model development. The assessment of AI model performance was evaluated with sensitivity, precision, and F1 scores. Results When the performance of the proposed AI model for detecting dental conditions in panoramic radiographs was evaluated, the best sensitivity values were obtained from the crown, implant, and impacted tooth as 0.9674, 0.9615, and 0.9658, respectively. The worst sensitivity values were obtained from the pontic, caries, and dental calculus, as 0.7738, 0.3026, and 0.0934, respectively. The best precision values were obtained from pontic, implant, implant-supported crown as 0.8783, 0.9259, and 0.8947, respectively. The worst precision values were obtained from residual root, caries, and dental calculus, as 0.6764, 0.5096, and 0.1923, respectively. The most successful F1 Scores were obtained from the implant, crown, and implant-supported crown, as 0.9433, 0.9122, and 0.8947, respectively. Conclusion The proposed AI model has promising results at detecting dental conditions in panoramic radiographs, except for caries and dental calculus. Thanks to the improvement of AI models in all areas of dental radiology, we predict that they will help physicians in panoramic diagnosis and treatment planning, as well as digital-based student education, especially during the pandemic period.

Objectives: Coronary artery calcium measured by CT predicts future coronary events. Similarly, carotid artery calcium on dental panoramic radiographs has been associated with increased cardiovascular events. Pre-procedural assessment of candidates for valve replacement in our institution includes panoramic radiographs and chest tomography. We aimed to assess the association of carotid calcium on panoramic radiographs with coronary artery calcium on chest tomography. Methods: Paired pre-procedural panoramic radiographs and chest tomography scans were done in 177 consecutive patients between October 2016 and October 2017. Carotid calcium was quantified using NIH’s ImageJ. Coronary artery calcium was quantified by the Agatston score using Philips Intellispace portal, v. 8.0.1.20640. Results: Carotid calcium maximal intensity, area and perimeter were higher among patients with high coronary artery calcium. Non-zero carotid calcium was found in half of patients with high coronary artery calcium, doubling prevalence of low coronary artery calcium. Conclusion: Carotid calcium identified in panoramic radiographs was associated with high coronary artery calcium. Awareness of carotid calcium recognized by dental practitioners in low-cost, low radiation and commonly done panoramic radiographs may be useful to identify patients at risk of coronary disease with potential future cardiovascular events.

The use of radiographic indices is noticeably diminished due to the lack of simplicity and standardisation among the existing ones. The aim of this study was to introduce a radiographic index to aid clinicians in determining the extent and severity of interproximal alveolar bone loss (iABL), in relation to individual root lengths, among patients suffering from periodontitis. A retrospective analysis of 50 anonymised dental panoramic tomograms (DPTs) of patients with periodontitis was conducted. Visual interpretation of iABL was recorded by a single investigator and by 20 volunteering clinicians for the ‘worst site’ in each quintet. Results were compared to a gold standard quantification method. Intra-examiner and inter-examiner agreement were measured using the Kappa coefficient and the intra-class correlation coefficient, respectively. Validity was assessed using Cramér’s V test. The mean intra-examiner agreement on the severity and pattern of iABL was 0.808 (K) and 0.802 (K), respectively. A stronger overall inter-examiner agreement was noted when the severity in contrast to the pattern of iABL and presence/absence of furcation involvement were analysed. The statistically significant total mean agreement values from this correlation coefficient were 0.892 and 0.739, respectively. A very strong association between all the visual interpretations carried out by all participants and the gold standard measurements was evident. Within the limitations of this study, the proposed radiographic index may serve as a simple, yet valid and reliable, adjunctive screening tool to further assist clinicians in determining the extent and severity of iABL in patients with periodontitis.

Maxillary and mandibular incisors have increased risk for severe orthodontically induced inflammatory root resorption. A patient-related risk factor is aberrant root morphology. This study aimed to assess the frequency of detection of different root morphologies in anterior teeth using dental panoramic tomography (DPT) and long cone periapical radiographs (LCPAs). A retrospective cross-sectional design was used to assess a sample of 50 consecutive pre-treatment radiographic records of patients from a specialist orthodontic practice in Adelaide, Australia. A reference guide was developed that included three previously unreported morphologies: pipette and bent, bent and pointed, bent and blunt. Two trained and calibrated assessors examined each record against the inclusion criteria, then independently assessed each anterior tooth from DPTs and LCPAs to detect the type of root morphology present. Data were analysed using the chi-square statistical test. Radiographic records for 48 patients (48 DPTs and 161 LCPAs) were eligible, with 355 and 426 teeth on DPTs and LCPAs, respectively, included for assessment. Normal root morphology (119 teeth) was commonly observed in DPTs, while bent (154 teeth) was frequently observed using LCPAs. Mandibular incisors often had normal morphology in DPTs but bent in LCPAs. Bent was the most common morphology in maxillary lateral incisors using DPT and LCPAs, although maxillary centrals were mostly normal in DPTs but pointed in LCPAs. Differences using the two image acquisition methods were highly significant (p < 0.01). Aberrant root morphologies are more easily detected in anterior teeth using LCPAs compared to DPTs.

Dental radiography plays an important role in clinical diagnosis, treatment and making decisions. In recent years, efforts have been made on developing techniques to detect objects in images. The aim of this study was to detect the absence or presence of teeth using an effective convolutional neural network, which reduces calculation times and has success rates greater than 95%. A total of 8000 dental panoramic images were collected. Each image and each tooth was categorized, independently and manually, by two experts with more than three years of experience in general dentistry. The neural network used consists of two main layers: object detection and classification, which is the support of the previous one. A Matterport Mask RCNN was employed in the object detection. A ResNet (Atrous Convolution) was employed in the classification layer. The neural model achieved a total loss of 0.76% (accuracy of 99.24%). The architecture used in the present study returned an almost perfect accuracy in detecting teeth on images from different devices and different pathologies and ages.

During routine imaging of the craniofacial region, it is recognised that some “cosmetic” procedures with metallic insertions can be revealed radiographically. These objects however make it difficult to obtain a good interpretation of anatomical structures for management of diseases. A 58-year-old female patient visited a private dental facility in Kuala Lumpur for prosthodontic replacement of missing teeth. The dental panoramic image revealed generalized bone loss and numerous unusual multiple thread-like radioopacities. These gold threads made radiographic evaluation difficult and complicated the process of treatment planning for dental implant placement advocated for this patient.

Dental image analysis is important for orthodontics, forensics, and dental treatments like cavity restoration or implants. In order to build a computer-aided diagnosis system for dental analysis, localization and numbering of teeth are crucial. In this study, we propose to use a popular deep learning technique, Mask regions with convolutional neural network features (RCNN), for simultaneous detection, segmentation, and numbering of teeth in panoramic X-ray images. Multiclass labeling is performed by Mask RCNN by giving a unique class name to each tooth type. After classification, postprocessing is performed for numbering teeth according to detected labels and dental chart. The proposed method is trained on 200 images and tested on 278 panoramic dental images. The average tooth detection accuracy is 0.98, and F1 score for segmentation is 0.93.

Aim: To review and search studies that evaluated the radiation doses and risks from dental panoramic radiography (DPR). This review may answer the following question “does panoramic dental radiation expose patients to health risks? Materiels and Methods: The search strategy was performed using the databases including GoogleScholar, PubMed, Science Direct and Nature from 2010 until september 2020. The search was limited to articles published in English. The 1031 titles that appeared, 15 fulfilled the criteria and were included in the review. Results: in 10 studies, the effective dose from DPRs was in the range of 5-49µSv, depending on the panoramic equipment used. The salivary glands received the highest absorbed doses (359-3044,3 μGy) and the thyroid gland absorbed doses were (40-250µGy). In terms of health risk, according to 5 studies, DPRs can induce genotoxic effects in oral epithelial cells and even induce cytotoxic effects leading to cell death. In 2 studies, women are at higher risk than men. The overall risk of cancer from radiation in children was more than adult. Conclusion: DPR can expose patient to health risks. It should only be applied when necessary, using radiation protection criteria.

Dentists have to check a large number of panoramic X-ray images every day. It takes much time and may cause human-made error. There are some studies in the literature to detect teeth by using deep learning-based techniques automatically. However, due to the limitations of object detection performance, it may provide multiple candidates. This paper proposes an automated teeth recognition method that refines the multiple detected candidates in panoramic X-ray images based on an optimization method with a prior knowledge model. The proposed method detects teeth candidates through faster R-CNN, and then determines the appropriate candidates by optimizing an objective function. The objective function evaluates the teeth's relative position concerning other teeth based on the prior knowledge model. Use 1000 images, the proposed method's accuracy was 0.94, and was higher than 0.92 with a method without optimization method. The experimental results verified that the proposed method could perform better than the stand-alone faster R-CNN technique.

Objectives: To investigate the effect of reducing scanning parameters of digital dental panoramic tomogram (DPT) and lateral cephalometric (LC) radiographs on quality and diagnostic performance of the images. Design: Single-centre prospective two-arm parallel randomised controlled trial. Setting: Orthodontic Clinic, Faculty of Dentistry, Universiti Teknologi MARA, Selangor, Malaysia. Participants: Adult orthodontic patients aged 18–35 years, indicated for DPT and LC, who were fit and healthy with a body mass index of 18.5–25.0, not contraindicated to radiographic examination, not pregnant, and did not have a history of facial or skeletal abnormalities or bone diseases were included. Methods: Thirty-eight adult orthodontic patients were randomised into control and intervention groups. DPT and LC radiographs in the control group were obtained using standard scanning parameters as prescribed by the manufacturer using Orthopantomograph® OP300 by Instrumentarium. Scanning parameters in the intervention group were reduced by 60% for DPT (60 kV, 3.2 mA) and 30% for LC (85 kV, 8 mA). A five-point rating scale was used for the assessment of image quality. Images were evaluated for diagnostic performance by detection of anatomical landmarks. Mann–Whitney test was performed to compare the quality and diagnostic performance of the images and the observer agreement was assessed using the intraclass correlation coefficient (ICC). Results: For image quality, the control group produced slightly lower median scores (DPT 2.0, LC 2.0) compared to the intervention group (DPT 2.0, LC 3.0). For diagnostic performance, both groups showed similar median scores (DPT 21.0, LC 32.0). The differences between control and intervention groups for both modalities were not statistically significant. The average scores for intra-observer agreement were excellent (ICC 0.917) and inter-observer agreement was good (ICC 0.822). Conclusion: Minimising radiation exposure by reducing scanning parameters on digital DPT by 60% and LC by 30% on Intsrumentarium 300 OP did not affect the quality and diagnostic performance of the images. Thus, scanning parameters on digital DPT and LC should be reduced when taking radiographs.

Dental panoramic radiography (DPR) images have recently attracted increasing attention in osteoporosis analysis because of their inner correlation. Many approaches leverage machine learning techniques (e.g., deep convolutional neural networks (CNNs)) to study DPR images of a patient to provide initial analysis of osteoporosis, which demonstrates promising results and significantly reduces financial cost. However, these methods heavily rely on the trabecula landmarks of DPR images that requires a large amount of manual annotations by dentist, and thus are limited in practical application. Addressing this issue, we propose to automatically detect trabecular landmarks in DPR images. In specific, we first apply CNNs-based detector for trabecular landmark detection and analyze its limitations. Using CNNs-based detection as a baseline, we then introduce a statistic shape model (SSM) for trabecular landmark detection by taking advantage of spatial distribution prior of trabecular landmarks in DPR images and their structural relations. In experiment on 108 images, our solution outperforms CNNs-based detector. Moreover, compared to CNN-based detectors, our method avoids the needs of vast training samples, which is more practical in application.

Dental x-ray imaging helps dentists and radiologists to diagnose dental diseases and to provide patients with treatment plannings. In many cases, dental diseases are hard to detect by relying only on visual inspection. Therefore, automating the diagnosis process has been a topic of interest for dental problems. Teeth extraction is the basic task needed for nearly all dentistry decision support systems relying on radiographic images as the inputs. The most challenging type of image to perform extraction on is the panoramic image since it includes other parts of the patient's mouth, and structures lack explicit boundaries. The proposed method in this paper is the first automated teeth extraction system from dental panoramic images using evolutionary algorithms. First, the jaw is extracted from the main image. Then, upper and lower jaws are separated, followed by a genetic algorithm to detect teeth gap valleys. The method is assessed applying to 42 images, where the perceived accuracy is 81.14% for upper jaws and 73.63% for lower jaws, which is comparable with previous methods used on more straightforward image types.

Objectives Among common head and neck cancers (HNCs), oropharyngeal cancer (OPC) patients have been identified as having a better dentition than many other tumour subsites. OPC consists of human papillomavirus (HPV)-positive and negative groups with different prognosis. The purpose of this study is to explore the presenting dental status of OPC patients based on HPV status at the pre-radiotherapy phase.Materials and methods The study reviewed the dental panoramic radiographs of OPC patients seen at a dedicated pre-radiotherapy dental assessment clinic from 2011-2017. Only patients planned for intensity-modulated radiotherapy treatment were included within this study. Relevant dental and oncological data were collected.Results A total of 316 patients with known HPV status (215 positive; 101 negative) were included for analysis. HPV-positive patients had significantly more teeth on attendance than HPV-negative patients (22.3 vs 19.0, p = 0.0000) and horizontal bone loss was less severe compared to HPV-negative patients (p = 0.0000). HPV-positive males and patients in the 55-64 decade presented with the best and most complex dentition.Conclusion The rise of OPC with the prospect of long survival, particularly in HPV-positive patients, requires a dentition with adequate function and subsequent maintenance. The current study demonstrated that these patients have a complex dentition presenting new challenges to the dentist. This may explain in part the elevated osteoradionecrosis rate seen in this tumour group.

Summary Objectives To analyse prevalence, pattern, and severity of taurodontism in individuals with Van der Woude syndrome (VWS) exhibiting cleft palate and compare with aged-matched non-syndromic cleft palate (NSCP) and non-cleft controls. Materials and methods One hundred and seventy-eight dental panoramic tomographs (DPTs) (105 girls and 73 boys) consisting of 42 VWS patients (x¯=8.55±1.02 years), 42 NSCP patients (x¯=8.59±1.02 years), and 94 normative non-cleft children (x¯=8.79±1.16 years) were assessed and their first permanent molars evaluated. Measurement 3 of the taurodontism index developed by Shifman and Chanannel with the Tulensalo modification was used. Prevalence, pattern, and severity were compared between groups. Statistical differences were determined by one-way analysis of variance and Fisher test. Repeatability was calculated by Cohens Kappa test. Results The prevalence of taurodontic molars was 59.5% in VWS, 45.2% in NSCP, and 26.6% in non-cleft controls. The prevalence and severity of taurodontism in VWS and NSCP were significantly higher than in non-cleft children in all first permanent molars. There was no significant difference in prevalence and severity between VWS and NSCP. The odds for having taurodontism in the VWS group was approximately double compared to the NSCP group. Most of the taurodontic molars showed hypotaurodontism and taurodontism occurred bilaterally more frequently than unilaterally. Conclusion This study shows a higher prevalence of taurodontism in VWS and NSCP. Most taurodontic molars are hypotaurodontic and most occur bilaterally.

Objective:The purpose of this study was to determine, if a dental panoramic tomograph (DPT) is appropriate for every young adult due to third molars. Materials and methods:The study sample consisted of 217 university students (20% men and 80% women; mean age 20.7 years; SD +/- 0.6 years) and included a questionnaire about symptoms caused by third molars, clinical oral examination of third molars, and a DPT. Subjects were divided into the following groups: subjects with a clinical indication for a DPT and subjects without such indication. The DPTs were then examined for findings regarding third molars. Results:Clinical indication for a DPT was observed in 64% of the subjects. Radiography revealed an additional 1.4% of the subjects with >= 1 radiographic signs of disease in relation to their third molars. Also, an additional 27% of the subjects had >= 1 other radiographic findings in relation to third molars that may have affected the clinical decision making. Conclusions:In our study population, clinically undetectable pathology cannot be considered as an indication for a DPT. However, if prevailing clinical practice supports preventive removals and detecting or monitoring of unerupted third molars, a referral to DPT can be considered as good clinical practice.

Objectives Age estimation is widely applied in the field of orthodontics, pediatric dentistry, and forensic science. Dental age estimation by the radiological method is frequently used because of its convenience and noninvasiveness. However, there are not enough suitable methods for eastern Chinese children. This study aimed to establish a modified formula for eastern Chinese children according to the Demirjian method and then compared the accuracy of the modified method with the Demirjian method and Willems method. Materials and methods A total of 2367 dental panoramic radiographs from individuals aged 5–16 years of eastern China were collected as samples. Age estimation was conducted using the Demirjian and Willems methods. The polynomial curve fitting method was used to modify the Demirjian method to improve its application to the eastern Chinese children. The paired t test and accuracy ratio were used to compare the applicability of the modified methods with two commonly used methods. Results The mean chronological age (CA) of the subjects was 11.20 ± 3.29 years for boys and 10.99 ± 3.12 years for girls. The mean difference values between the CA and dental age (DA) (CA–DA) using the Demirjian and Willems methods were 0.73 and 0.7 for boys, respectively, and both 0.79 for girls. The modified method using the polynomial curve fitting presented a smaller underestimation compared with CA for both boys (0.04 years) and girls (0.09 years), which showed a high suitability to Chinese children to some extent. Conclusions The Willems method was more accurate in estimating DA compared with the Demirjian method. However, the modified method was more accurate than the two methods; therefore, it can be used in eastern Chinese children. Clinical relevance It was thought to be a non-invasive, convenient, and efficient method to connect DA and CA. By estimating dental age, pediatrist, and orthodontists can better understand the development of permanent teeth and provide a more accurate orthodontic treatment time and treatment plan to children patients.

Dental panoramic X-ray imaging is a popular diagnostic method owing to its very small dose of radiation. For an automated computer-aided diagnosis system in dental clinics, automatic detection and identification of individual teeth from panoramic X-ray images are critical prerequisites. In this study, we propose a point-wise tooth localization neural network by introducing a spatial distance regularization loss. The proposed network initially performs center point regression for all the anatomical teeth (i.e., 32 points), which automatically identifies each tooth. A novel distance regularization penalty is employed on the 32 points by considering $L_2$ regularization loss of Laplacian on spatial distances. Subsequently, teeth boxes are individually localized using a cascaded neural network on a patch basis. A multitask offset training is employed on the final output to improve the localization accuracy. Our method successfully localizes not only the existing teeth but also missing teeth; consequently, highly accurate detection and identification are achieved. The experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches by increasing the average precision of teeth detection by 15.71% compared to the best performing method. The accuracy of identification achieved a precision of 0.997 and recall value of 0.972. Moreover, the proposed network does not require any additional identification algorithm owing to the preceding regression of the fixed 32 points regardless of the existence of the teeth., Comment: 10 pages, 7 figures

Objective To evaluate the effects of functional appliance treatment on mandibular trabecular structure using fractal dimension (FD) analysis of dental panoramic radiographs. Materials and Methods This study was conducted using digital panoramic radiographs of 45 patients with Class II malocclusion treated with functional appliances (treatment group, mean age: 11.39 ± 0.97 years; 23 girls, 22 boys) acquired before (T0) and after (T1) treatment and the panoramic radiographs of 45 control subjects who had undergone no orthodontic treatment (control group, mean age: 11.31 ± 0.87 years; 23 girls, 22 boys). FD values in the condylar process, mandibular corpus, and mandibular angle were analyzed from the panoramic radiographs of both groups. Results Analysis of changes in FD between T0 and T1 revealed significant increases in the FD values of the right and left condylar processes and right mandibular corpus in the treatment group (P < .001) and in the right condylar process in the control group (P < .05). Between-group comparisons demonstrated that the treatment group showed greater changes in the condylar process (right, P < .001; left, P < .05) and right mandibular corpus (P < .05) compared to controls. Correlation analysis between the cephalometric and FD changes in the treatment group showed the right condylar process changes were negatively correlated with GoGn/SN angle (P < .05) and positively correlated with Co-Go (P < .05), although these correlations were weak. Conclusions FD analysis demonstrated significant changes in trabeculation of the condyle and mandibular corpus in the treatment group compared to the control group. Functional appliance treatment may lead to skeletal correction by altering skeletal form and trabeculation of the mandibular bone.