Determination of safety margin of nasal septum osteotomy for sphenoid sinus in cleft lip and palate patients.

BACKGROUND: Nasal septum osteotomy is used for separating the nasal septum and maxilla during a Le Fort I osteotomy. If this osteotomy is applied too high or is tilted into the nasal cavity, the sphenoid sinus and various adjacent vital structures may be damaged, and serious bleeding, neurological complications, blindness or even death may occur. The aim of this study is to determine the safety margin of the nasal septum osteotomy for sphenoid sinus during the Le Fort I surgery in cleft lip and palate (CLP) patients. METHODS: Twenty cleft lip and palate (the CLP group) and 20 healthy individuals (the control group) were included in this study. Three values (two lines and an angle) were measured by cone beam computed tomography (CBCT). The first line is the line passing through the junction of the spina nasalis anterior point and the lower point of the perpendicular lamina of the palatine bone. The undersired line is the line passing through the junction of the spina nasalis anterior point and the lower anterior border of the base of the sphenoid sinus. The osteotomy angle is the angle between these two lines. RESULTS: In the control group; a surgical line of 44.11-61.14 mm (mean 51.91 ± 4.32), an undesired line of 52.48-69.58 mm (mean 59.14 ± 5.08) and an angle of 18.22-27.270 (mean 22.66 ± 2.55) were found, while in the CLP group, a surgical line of 34.53-51.16 mm (mean 43.38 ± 4.79), an undesired line of 46.86-61.35 mm (mean 55.02 ± 3.24) and an angle of 17.60-28.810 (mean 22.60 ± 2.81) were found. CONCLUSIONS: Although the angle to the sphenoid sinus was not significantly affected by CLP, careful planning and consideration of these anatomical differences are crucial to prevent complications and ensure the safety of Le Fort I surgery in CLP patients. Further research with larger sample sizes and subgroup analysis of unilateral and bilateral CLP cases is needed to improve our understanding of these anatomical variations and improve surgical approaches to individuals with CLP undergoing orthognathic procedures.

Comparative study of anatomical features of primary and accessory maxillary ostia between patients with obstructive sleep apnea and healthy controls.

PURPOSE: The purpose of this study was to examine how the size and shape of the maxillary sinus and its ostia (the primary maxillary ostium and accessory maxillary ostium) relate to each other in patients with OSA using computed tomography (CT) scans. Additionally, the study aimed to explore whether or not obstructive sleep apnea (OSA) had an effect on these structures. METHODS: CT images of patients diagnosed with OSAS and healthy participants were evaluated to compare the patency, location, dimension, and presence of PMOs and AMOs using the Mann-Whitney U, Student t, and chi-square tests. Also, intragroup correlations were analyzed by Spearman's correlation test. RESULTS: Among 139 patients with OSA and healthy controls, there were significant variations in the average length (p = 0.001) and width (p = 0.008) of PMOs among the study groups. The mean maxillary sinus volume was significantly decreased in the OSA group (p = 0.001). A significant decrease in the maxillary sinus volume was observed in the OSA group (p = 0.001). In the OSA group, a significant correlation was observed between PMO obstruction and the presence of AMO (p = 0.004). The healthy group had significant correlations (r = 0.755, p = 0.000) between the vertical height and the distance between PMO and the maxillary sinus floor. Correlation analyses revealed positive, strong correlations between study variables such as the mean length and width of AMO and the vertical height of the maxillary sinus (r = 0.566, p = 0.000) in the OSA group. CONCLUSIONS: The current study indicated significant differences in sinus volume, PMO occlusion, and AMO-related dimensions between patients with OSA and healthy controls.

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population.

BACKGROUND: Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and mandibular anatomic structures observed on panoramic radiographs in children using artificial intelligence. METHODS: A total of 981 mixed images of pediatric patients for 9 different pediatric anatomic landmarks including maxillary sinus, orbita, mandibular canal, mental foramen, foramen mandible, incisura mandible, articular eminence, condylar and coronoid processes were labelled, the training was carried out using 2D convolutional neural networks (CNN) architectures, by giving 500 training epochs and Pytorch-implemented YOLO-v5 models were produced. The success rate of the AI model prediction was tested on a 10% test data set. RESULTS: A total of 14,804 labels including maxillary sinus (1922), orbita (1944), mandibular canal (1879), mental foramen (884), foramen mandible (1885), incisura mandible (1922), articular eminence (1645), condylar (1733) and coronoid (990) processes were made. The most successful F1 Scores were obtained from orbita (1), incisura mandible (0.99), maxillary sinus (0.98), and mandibular canal (0.97). The best sensitivity values were obtained from orbita, maxillary sinus, mandibular canal, incisura mandible, and condylar process. The worst sensitivity values were obtained from mental foramen (0.92) and articular eminence (0.92). CONCLUSIONS: The regular and standardized labelling, the relatively larger areas, and the success of the YOLO-v5 algorithm contributed to obtaining these successful results. Automatic segmentation of these structures will save time for physicians in clinical diagnosis and will increase the visibility of pathologies related to structures and the awareness of physicians.

Assessing the Effectiveness of Artificial Intelligence Models for Detecting Alveolar Bone Loss in Periodontal Disease: A Panoramic Radiograph Study.

The assessment of alveolar bone loss, a crucial element of the periodontium, plays a vital role in the diagnosis of periodontitis and the prognosis of the disease. In dentistry, artificial intelligence (AI) applications have demonstrated practical and efficient diagnostic capabilities, leveraging machine learning and cognitive problem-solving functions that mimic human abilities. This study aims to evaluate the effectiveness of AI models in identifying alveolar bone loss as present or absent across different regions. To achieve this goal, alveolar bone loss models were generated using the PyTorch-based YOLO-v5 model implemented via CranioCatch software, detecting periodontal bone loss areas and labeling them using the segmentation method on 685 panoramic radiographs. Besides general evaluation, models were grouped according to subregions (incisors, canines, premolars, and molars) to provide a targeted evaluation. Our findings reveal that the lowest sensitivity and F1 score values were associated with total alveolar bone loss, while the highest values were observed in the maxillary incisor region. It shows that artificial intelligence has a high potential in analytical studies evaluating periodontal bone loss situations. Considering the limited amount of data, it is predicted that this success will increase with the provision of machine learning by using a more comprehensive data set in further studies.

Clinical and ultrasonographic evaluation of ultrasound-guided single puncture temporomandibular joint arthrocentesis.

OBJECTIVE: To evaluate the effects of successful TMJ treatment on relief of pain, improvement of mandibular movement and capsular width with clinical and ultrasonography (US) findings. In this study, TMJ changes were evaluated by clinical and US examination after US-guided single-puncture arthrocentesis, which represents a novel approach. METHODS: Clinical measurements were obtained before each procedure and at 1 day, 7 days, and 3 months thereafter. Capsular width was measured via the US at the 3-month follow-up. RESULTS: Significant improvements were evident at the short term of 3 months post-arthrocentesis with supportive treatment, including splint therapy and jaw exercises. CONCLUSION: Arthrocentesis in conjunction with splint therapy and jaw exercises demonstrated significant clinical improvement at the short-term follow-up of 3 months. US imaging can be helpful for follow-up evaluation of the pre- and post-treatment capsule width. Longer follow-up studies are necessary to validate the effectiveness of this treatment protocol.

Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm.

OBJECTIVES: Artificial intelligence (AI) techniques like convolutional neural network (CNN) are a promising breakthrough that can help clinicians analyze medical imaging, diagnose taurodontism, and make therapeutic decisions. The purpose of the study is to develop and evaluate the function of CNN-based AI model to diagnose teeth with taurodontism in panoramic radiography. METHODS: 434 anonymized, mixed-sized panoramic radiography images over the age of 13 years were used to develop automatic taurodont tooth segmentation models using a Pytorch implemented U-Net model. Datasets were split into train, validation, and test groups of both normal and masked images. The data augmentation method was applied to images of trainings and validation groups with vertical flip images, horizontal flip images, and both flip images. The Confusion Matrix was used to determine the model performance. RESULTS: Among the 43 test group images with 126 labels, there were 109 true positives, 29 false positives, and 17 false negatives. The sensitivity, precision, and F1-score values of taurodont tooth segmentation were 0.8650, 0.7898, and 0.8257, respectively. CONCLUSIONS: CNN's ability to identify taurodontism produced almost identical results to the labeled training data, and the CNN system achieved close to the expert level results in its ability to detect the taurodontism of teeth.

A Deep Learning Model for Idiopathic Osteosclerosis Detection on Panoramic Radiographs.

OBJECTIVE: The purpose of the study was to create an artificial intelligence (AI) system for detecting idiopathic osteosclerosis (IO) on panoramic radiographs for automatic, routine, and simple evaluations. SUBJECT AND METHODS: In this study, a deep learning method was carried out with panoramic radiographs obtained from healthy patients. A total of 493 anonymized panoramic radiographs were used to develop the AI system (CranioCatch, Eskisehir, Turkey) for the detection of IOs. The panoramic radiographs were acquired from the radiology archives of the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University. GoogLeNet Inception v2 model implemented with TensorFlow library was used for the detection of IOs. Confusion matrix was used to predict model achievements. RESULTS: Fifty IOs were detected accurately by the AI model from the 52 test images which had 57 IOs. The sensitivity, precision, and F-measure values were 0.88, 0.83, and 0.86, respectively. CONCLUSION: Deep learning-based AI algorithm has the potential to detect IOs accurately on panoramic radiographs. AI systems may reduce the workload of dentists in terms of diagnostic efforts.

A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs.

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.

Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system.

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.

Performance of a convolutional neural network algorithm for tooth detection and numbering on periapical radiographs.

OBJECTIVES: The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images. METHODS: The data sets of 1686 randomly selected periapical radiographs of patients were collected retrospectively. A pre-trained model (GoogLeNet Inception v3 CNN) was employed for pre-processing, and transfer learning techniques were applied for data set training. The algorithm consisted of: (1) the Jaw classification model, (2) Region detection models, and (3) the Final algorithm using all models. Finally, an analysis of the latest model has been integrated alongside the others. The sensitivity, precision, true-positive rate, and false-positive/negative rate were computed to analyze the performance of the algorithm using a confusion matrix. RESULTS: An artificial intelligence algorithm (CranioCatch, Eskisehir-Turkey) was designed based on R-CNN inception architecture to automatically detect and number the teeth on periapical images. Of 864 teeth in 156 periapical radiographs, 668 were correctly numbered in the test data set. The F1 score, precision, and sensitivity were 0.8720, 0.7812, and 0.9867, respectively. CONCLUSION: The study demonstrated the potential accuracy and efficiency of the CNN algorithm for detecting and numbering teeth. The deep learning-based methods can help clinicians reduce workloads, improve dental records, and reduce turnaround time for urgent cases. This architecture might also contribute to forensic science.

Deep-learning approach for caries detection and segmentation on dental bitewing radiographs.

OBJECTIVES: The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer. METHODS: A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively. RESULTS: The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists. CONCLUSION: CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.

An artificial intelligence approach to automatic tooth detection and numbering in panoramic radiographs.

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.

A deep learning approach for dental implant planning in cone-beam computed tomography images.

BACKGROUND: The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images. METHODS: Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual assessment method using InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. Following, all evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc., San Francisco, USA) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual assessment and AI methods were compared using Bland-Altman analysis and Wilcoxon signed rank test. RESULTS: In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p > 0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p < 0.001). Also, the percentage of right detection was 72.2% for canals, 66.4% for sinuses/fossae and 95.3% for missing tooth regions. CONCLUSIONS: Development of AI systems and their using in future for implant planning will both facilitate the work of physicians and will be a support mechanism in implantology practice to physicians.

Effects of bruxism on temporomandibular joint internal derangement in patients with unilateral temporomandibular joint pain: The role of magnetic resonance imaging diagnostics.

Objective: This study compared temporomandibular joint (TMJ) magnetic resonance imaging (MRI) findings between bruxism and control groups with unilateral TMJ pain as well as the TMJ MRI findings for the painful and non-painful sides of individuals in the two groups.Methods: Clinical and MRI findings of patients seen at Usak University, Dentistry Faculty, Department of Oral and Maxillofacial Surgery for unilateral TMJ pain between 2017 and 2020 were analyzed. Bruxism was diagnosed based on clinical findings and patient history. The MRI variables were disc/condyle relationship (normal, disc displacement with reduction, or disc displacement without reduction), disc structure (normal and abnormal), condyle degeneration type (normal, moderate, or severe), and joint effusion (absent or present). Pain was recorded based on a visual analog scale (VAS) numbered between 0 and 10. Statistical analyses were performed using IBM SPSS. The data were distributed non-normally according to the results of Kolmogorov-Smirnov tests. The Mann-Whitney U test was used to compare age and VAS. Chi-square tests were used to compare categorical variables. Statistical significance was defined as p < 0.05Results: This study assessed the MRI records of 558 cases of TMJ pain. No significant differences in disc/condyle relation, disc structure, condyle structure, or effusion were observed between the control and bruxism groups (p > 0.05). However, a significant difference in TMJ MRI findings was observed between the painful and non-painful sides of each individual in the control and bruxism groups (p = 0.001, p < 0.001 and p = 0.004, p < 0.001, respectively).Conclusion: The results of this study established a relationship between the painful side for each patient and TMJ MRI findings. In particular, individuals with bruxism had a higher rate of TMJ internal derangement and effusion on the painful side.

Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs.

OBJECTIVE: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs. METHODS AND MATERIALS: An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images. System performance was assessed using a confusion matrix. RESULTS: The AI system was successful in detecting and numbering the deciduous teeth of children as depicted on panoramic radiographs. The sensitivity and precision rates were high. The estimated sensitivity, precision, and F1 score were 0.9804, 0.9571, and 0.9686, respectively. CONCLUSION: Deep-learning-based AI models are a promising tool for the automated charting of panoramic dental radiographs from children. In addition to serving as a time-saving measure and an aid to clinicians, AI plays a valuable role in forensic identification.

Evaluation of the snowing-like ultrasonographic features of intraosseous jaw pathologies.

Aim of the study: Ultrasonographic examination of intraosseous jaw pathologies may reveal interesting incidental, mobile hyperechoic particles ("snowflakes") in anechoic areas. Purpose of this study is to explain and discuss this snowing-like ultrasonographic feature of intraosseous jaw pathologies. Material and methods: This study included 113 patients admitted to our clinic for examination: 43 (38.05%) males and 70 (61.9%) females with a mean age of 34.9 ± 17.2 years (range: 6-72 years). A total of 120 intraosseous lesions were evaluated prior to surgery using ultrasonography; these included non-neoplastic, odontogenic, and non-odontogenic lesions. Results: In total, 5 (4.1%) of the 120 lesions exhibited snowing-like feature on ultrasonography, including 2 (1.6% of total) of 3 incisive canal cysts, 2 (1.6% of total) of 7 dentigerous cysts, and 1 (0.8% of total) of 19 odontogenic keratocysts. Conclusions: Snowflakes evident on ultrasonography of intraosseous jaw lesions may be specific to certain pathologies. Future studies correlating radiologic and pathologic features of intraosseous jaw lesions should focus on ultrasonographic snowing-like appearance in different types of lesions and explore why they occur.

Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans.

PURPOSE: The aim of this study was to evaluate the diagnostic performance of artificial intelligence (AI) application evaluating of the impacted third molar teeth in Cone-beam Computed Tomography (CBCT) images. MATERIAL AND METHODS: In total, 130 third molar teeth (65 patients) were included in this retrospective study. Impaction detection, Impacted tooth numbers, root/canal numbers of teeth, relationship with adjacent anatomical structures (inferior alveolar canal and maxillary sinus) were compared between the human observer and AI application. Recorded parameters agreement between the human observer and AI application based on the deep-CNN system was evaluated using the Kappa analysis. RESULTS: In total, 112 teeth (86.2%) were detected as impacted by AI. The number of roots was correctly determined in 99 teeth (78.6%) and the number of canals in 82 teeth (68.1%). There was a good agreement in the determination of the inferior alveolar canal in relation to the mandibular impacted third molars (kappa: 0.762) as well as the number of roots detection (kappa: 0.620). Similarly, there was an excellent agreement in relation to maxillary impacted third molar and the maxillary sinus (kappa: 0.860). For the maxillary molar canal number detection, a moderate agreement was found between the human observer and AI examinations (kappa: 0.424). CONCLUSIONS: Artificial Intelligence (AI) application showed high accuracy values in the detection of impacted third molar teeth and their relationship to anatomical structures.

A new classification proposal for sphenoid sinus pneumatization: a retrospective radio-anatomic study.

OBJECTIVES: The sphenoid sinus variations are very diverse and the frequency of these sinus variations is high. During operations involving the sphenoid sinus, such as pituitary surgeries, the surgeon should have detailed information about these variations. The aim of this study is to reclassify the sphenoid sinus pneumatizations in detail and to evaluate the incidence of pneumatization types in a Turkish population according to this classification. METHODS: New classification proposal was made. In accordance with the proposed new classification, sphenoid sinus pneumatizations were evaluated on CBCT images. RESULTS: When the posteroanterior pneumatization of 128 patients was evaluated; 2.3% conchal, 3.9% presellar, 35.9% sellar, and 57.8% postsellar pneumatization was detected. Of these cases, 28.9% had anterior pneumatization on the right and 23.4% on the left. When lateral direction pneumatizations were evaluated, lateral body type was found to be the most common on both right (44.1%) and left (42.5%) sides. CONCLUSION: In this study, sphenoid sinus pneumatizations were evaluated three-dimensionally with the help of CBCT, and a new classification suggestion was made to eliminate the classification confusion we encountered in our previous studies. Pneumatizations and variations can affect the field of operation and even change planning. It should be taken into account that the paranasal sinuses may have variations due to their surgical importance and their close association with many vital structures.

An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs.

OBJECTIVES: Radiological examination has an important place in dental practice, and it is frequently used in intraoral imaging. The correct numbering of teeth on radiographs is a routine practice that takes time for the dentist. This study aimed to propose an automatic detection system for the numbering of teeth in bitewing images using a faster Region-based Convolutional Neural Networks (R-CNN) method. METHODS: The study included 1125 bite-wing radiographs of patients who attended the Faculty of Dentistry of Ordu University from 2018 to 2019. A faster R-CNN an advanced object identification method was used to identify the teeth. The confusion matrix was used as a metric and to evaluate the success of the model. RESULTS: The deep CNN system (CranioCatch, Eskisehir, Turkey) was used to detect and number teeth in bitewing radiographs. Of 715 teeth in 109 bite-wing images, 697 were correctly numbered in the test data set. The F1 score, precision and sensitivity were 0.9515, 0.9293 and 0.9748, respectively. CONCLUSIONS: A CNN approach for the analysis of bitewing images shows promise for detecting and numbering teeth. This method can save dentists time by automatically preparing dental charts.

Evaluation of gubernacular tract with cone beam computed tomography in impacted supernumerary teeth

The gubernacular canal or gubernacular tract is filled by the gubernacular cord, which includes fibrous connective tissue containing peripheral nerves, blood and lymphatic ducts besides the epithelial cells from the fragmented dental laminae, including epithelial growth factor. The purpose of this study was to evaluate the gubernacular tract in unerupted supernumerary teeth by cone beam computed tomography. Sixty-four unerupted supernumerary teeth were selected from 44 patients (21 females, 23 males, 12-68 years). Gubernacular tract characteristics were evaluated in five different groups: No alteration, bending of gubernacular tract, contraction of gubernacular tract, obliterations of gubernacular tract, difference between erupted direction. Unerupted supernumerary teeth were classified according to their position. The presence and characteristics of the gubernacular tract in the supernumerary teeth were evaluated by cone beam computed tomography. In our study, the frequency of the gubernacular tract was found to be 31.7%. There was no significant difference between the presence of gubernacular tract and gender, age and gubernacular tract characteristics. It was found that gubernacular tract characteristics did not change according to gender, quadrant, age and unerupted positions. Cone beam computed tomography is an efficient method for the evaluation of the gubernacular tract in unerupted supernumerary teeth. Conducting these studies in larger populations will provide more detailed information about the prognosis of impacted supernumerary teeth

No disponible