Background: Panoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Panoramic radiography is frequently used in dental imaging due to its relatively low radiation dose, short imaging time, and low burden to the patient. We verified the diagnostic performance of an artificial intelligence (AI) system based on a deep convolutional neural network method to detect and number teeth on panoramic radiographs.Methods: The data set included 2482 anonymized panoramic radiographs from adults from the archive of Eskisehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology. A Faster R-CNN Inception v2 model was used to develop an AI algorithm (CranioCatch, Eskisehir, Turkey) to automatically detect and number teeth on panoramic radiographs. Human observation and AI methods were compared on a test data set consisting of 249 panoramic radiographs. True positive, false positive, and false negative rates were calculated for each quadrant of the jaws. The sensitivity, precision, and F-measure values were estimated using a confusion matrix.Results: The total numbers of true positive, false positive, and false negative results were 6940, 250, and 320 for all quadrants, respectively. Consequently, the estimated sensitivity, precision, and F-measure were 0.9559, 0.9652, and 0.9606, respectively.Conclusions: The deep convolutional neural network system was successful in detecting and numbering teeth. Clinicians can use AI systems to detect and number teeth on panoramic radiographs, which may eventually replace evaluation by human observers and support decision making.

The purpose of the paper was the assessment of the success of an artificial intelligence (AI) algorithm formed on a deep‐convolutional neural network (D‐CNN) model for the segmentation of apical lesions on dental panoramic radiographs. A total of 470 anonymized panoramic radiographs were used to progress the D‐CNN AI model based on the U‐Net algorithm (CranioCatch, Eskisehir, Turkey) for the segmentation of apical lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Eskisehir Osmangazi University. A U‐Net implemented with PyTorch model (version 1.4.0) was used for the segmentation of apical lesions. In the test data set, the AI model segmented 63 periapical lesions on 47 panoramic radiographs. The sensitivity, precision, and F1‐score for segmentation of periapical lesions at 70% IoU values were 0.92, 0.84, and 0.88, respectively. AI systems have the potential to overcome clinical problems. AI may facilitate the assessment of periapical pathology based on panoramic radiographs.

The purpose of the study was to investigate the relationship between Demirjian’s method and the improved cervical vertebrae maturation (CVM) method. The material consisted of the clinical files and panoramic and lateral cephalometric radiographs of 718 children (431 girls and 287 boys) aged from 6 to 17 years, inhabitants of the Mazovia region (Central Poland). Dental age according to Demirjian was estimated using panoramic radiographs and the cervical stages (CS) of the CVM were evaluated on cephalometric radiographs. Descriptive statistics of the chronological and dental ages of the patients for a particular CS of skeletal maturity was calculated for girls and boys separately. Linear regression analysis and correlation (Pearson’s r coefficient), as well as the Spearman rank correlation coefficient (R) were applied to measure the association between CS and dental calcification stages of all analysed teeth. A consistently earlier occurrence (by about 6 months) for each CS was observed in females. A moderate, but statistically significant, correlation between Demirjian’s dental developmental stages and CS was determined. The level of the correlation was different for individual teeth: the teeth showing the highest relationship with CVM were the second premolars and canines (in female and male subjects, respectively). The central incisor demonstrated the poorest correlation in both genders. The findings confirmed that both dental and skeletal maturity should be assessed if the maturity stage of a growing child is to be relevant to clinical practice. The results indicate the usefulness of dental calcification stages as a simple first-level diagnostic test to determine the skeletal maturity status of a subject.

Panoramic radiography is an extraoral radiography modality that provides two-dimensional information about the teeth and the maxillofacial skeleton. It is a valuable adjunct for diagnosis and treatment planning as it facilitates one-time imaging of all teeth, the mandible, parts of maxilla including a large part of the maxillary sinus, hard palate and temporomandibular joints (TMJs). As a tomographic image is prone to errors and artefacts, a good quality radiograph in most patients can be achieved by following the standard rules and through proper patient positioning. In this article, we will discuss indications for panoramic radiography imaging, steps in taking the radiograph, as well as limitations, pitfalls and complications of the procedure. Tomographic imaging of temporomandibular joint is also discussed.

While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.