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Background In current study we aimed to examine the effect of a low-level laser therapy on the pain, mouth opening and swelling of patients whose impacted 3rd molar tooth was extracted in addition measurement volumetrically to the edema with 3dMD face system. Methods It was surveyed 15 patients who had bilateral symmetric lower 3rd molars. Surgical sides of patients were randomly separated into two groups: the study group and the control group. It was applied extra oral low-level laser therapy (LLLT, 0.3 W, 40 s, 4 J/cm 2 ) to the study group ( n  = 15) after the surgical operation and on the 2nd day. Only routine postoperative recommendation (ice application) was made in the control ( n  = 15) group. The maximum mouth opening, pain level and facial swelling evaluated. 3dMD Face® (3dMD, Atlanta, GA) Photogrammetric System was used to evaluate volumetric changes of the swelling. Results There was no statistically significant difference in the edema and interincisal opening between the groups and the pain level in the laser group was significantly lower than in the control group on the 7 th postoperative day. Conclusions Although there were decreasing trismus, swelling, and pain level, with this LLLT, there was significant difference only in the 7th day pain level in the laser group compared with the control group.

Osteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lack of discernible symptoms. Hence, screening for osteoporosis with widely used dental panoramic radiographs would be very cost-effective and useful. In this study, we investigate the use of deep learning to classify osteoporosis from dental panoramic radiographs. In addition, the effect of adding clinical covariate data to the radiographic images on the identification performance was assessed. For objective labeling, a dataset containing 778 images was collected from patients who underwent both skeletal-bone-mineral density measurement and dental panoramic radiography at a single general hospital between 2014 and 2020. Osteoporosis was assessed from the dental panoramic radiographs using convolutional neural network (CNN) models, including EfficientNet-b0, -b3, and -b7 and ResNet-18, -50, and -152. An ensemble model was also constructed with clinical covariates added to each CNN. The ensemble model exhibited improved performance on all metrics for all CNNs, especially accuracy and AUC. The results show that deep learning using CNN can accurately classify osteoporosis from dental panoramic radiographs. Furthermore, it was shown that the accuracy can be improved using an ensemble model with patient covariates.

The aim of this study was to assess, via multi-slice helical computerized tomography (CT), the influence of the pure-phase multiporous beta–tricalcium phosphate (beta-TCP) on bone density around dental implants inserted into fresh extraction sockets. Twenty-eight patients (18 women and 10 men), indicated for extraction of their lower premolars and insertion of immediate dental implants, were included in this study. They were randomly divided into two equal groups (14 patients each). Group A received immediate dental implants without any filling material around the implants, while in group B, a pure-phase multiporous beta-TCP was gently packed into the bone gaps around the implants. Three and 6 months after loading the implants, a CT, sagittal and coronal, was made to measure the bone density around the implants. The results of the current study have shown that the mean values of the bone density measurements around the implants in group A were 1150 ± 205 (range, 645–1460) at 3 months and 1245 ± 165 (range, 884–1650) at 6 months after loading the implants. In group B, the mean values of the bone density measurements around the implants were 1280 ± 320 (range, 876–1790) and 1490 ± 358 (range, 1061–1965) at 3 and 6 months after loading the implants, respectively. The statistical analysis of the collected data showed a significant increase in the bone density measurements from 3 to 6 months only in group B ( P < .05). Also, the difference between group A and B in the bone density measurements around the implants was statistically significant ( P < .05) at only 6 months after loading. On the basis of the results presented in this study, it may be possible to mention that the pure-phase multiporous beta-TCP may enhance the bone density when inserted into the bone gaps around immediate dental implants.

Background This retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of periodontal bone losses and bone loss patterns. Methods A total of 1121 panoramic radiographs were used in this study. Bone losses in the maxilla and mandibula (total alveolar bone loss) ( n  = 2251), interdental bone losses ( n  = 25303), and furcation defects ( n  = 2815) were labeled using the segmentation method. In addition, interdental bone losses were divided into horizontal ( n  = 21839) and vertical ( n  = 3464) bone losses according to the defect patterns. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis. Results The system showed the highest diagnostic performance in the detection of total alveolar bone losses (AUC = 0.951) and the lowest in the detection of vertical bone losses (AUC = 0.733). The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone losses and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively). Conclusions AI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.

This study aimed to evaluate the accuracy of automated deep learning (DL) algorithm for identifying and classifying various types of dental implant systems (DIS) using a large-scale multicenter dataset. Dental implant radiographs of pos-implant surgery were collected from five college dental hospitals and 10 private dental clinics, and validated by the National Information Society Agency and the Korean Academy of Oral and Maxillofacial Implantology. The dataset contained a total of 156,965 panoramic and periapical radiographic images and comprised 10 manufacturers and 27 different types of DIS. The accuracy, precision, recall, F1 score, and confusion matrix were calculated to evaluate the classification performance of the automated DL algorithm. The performance metrics of the automated DL based on accuracy, precision, recall, and F1 score for 116,756 panoramic and 40,209 periapical radiographic images were 88.53%, 85.70%, 82.30%, and 84.00%, respectively. Using only panoramic images, the DL algorithm achieved 87.89% accuracy, 85.20% precision, 81.10% recall, and 83.10% F1 score, whereas the corresponding values using only periapical images achieved 86.87% accuracy, 84.40% precision, 81.70% recall, and 83.00% F1 score, respectively. Within the study limitations, automated DL shows a reliable classification accuracy based on large-scale and comprehensive datasets. Moreover, we observed no statistically significant difference in accuracy performance between the panoramic and periapical images. The clinical feasibility of the automated DL algorithm requires further confirmation using additional clinical datasets.

In this paper, a new powerful deep learning framework, named as DENTECT, is developed in order to instantly detect five different dental treatment approaches and simultaneously number the dentition based on the FDI notation on panoramic X-ray images. This makes DENTECT the first system that focuses on identification of multiple dental treatments; namely periapical lesion therapy, fillings, root canal treatment (RCT), surgical extraction, and conventional extraction all of which are accurately located within their corresponding borders and tooth numbers. Although DENTECT is trained on only 1005 images, the annotations supplied by experts provide satisfactory results for both treatment and enumeration detection. This framework carries out enumeration with an average precision (AP) score of 89.4% and performs treatment identification with a 59.0% AP score. Clinically, DENTECT is a practical and adoptable tool that accelerates the process of treatment planning with a level of accuracy which could compete with that of dental clinicians.

The approximity of the inferior alveolar nerve (IAN) to the roots of lower third molars (M3) is a risk factor for the occurrence of nerve damage and subsequent sensory disturbances of the lower lip and chin following the removal of third molars. To assess this risk, the identification of M3 and IAN on dental panoramic radiographs (OPG) is mandatory. In this study, we developed and validated an automated approach, based on deep-learning, to detect and segment the M3 and IAN on OPGs. As a reference, M3s and IAN were segmented manually on 81 OPGs. A deep-learning approach based on U-net was applied on the reference data to train the convolutional neural network (CNN) in the detection and segmentation of the M3 and IAN. Subsequently, the trained U-net was applied onto the original OPGs to detect and segment both structures. Dice-coefficients were calculated to quantify the degree of similarity between the manually and automatically segmented M3s and IAN. The mean dice-coefficients for M3s and IAN were 0.947 ± 0.033 and 0.847 ± 0.099, respectively. Deep-learning is an encouraging approach to segment anatomical structures and later on in clinical decision making, though further enhancement of the algorithm is advised to improve the accuracy.

This study aimed to develop an artificial intelligence (AI) model for the screening of degenerative joint disease (DJD) using temporomandibular joint (TMJ) panoramic radiography and joint noise data. A total of 2631 TMJ panoramic images were collected, resulting in a final dataset of 3908 images (2127 normal (N) and 1781 DJD (D)) after excluding indeterminate cases and errors. AI models using GoogleNet were evaluated with six different combinations of image data, clinician-detected crepitus, and patient-reported joint noise. The model that integrated all joint noise data with imaging demonstrated the highest performance, achieving an F1-score of 0.72. Another model, which incorporated both imaging and crepitus, also achieved the same F1-score but had lower D recall (0.55 vs. 0.67) and N precision (0.71 vs. 0.74). The AI models outperformed orofacial pain specialists when provided with imaging alone or in combination with all joint noise data. These findings suggest that AI-enhanced DJD diagnosis using TMJ panoramic radiography and joint noise data offers a promising approach for early detection and improved patient care. The results underscore AI’s capability to integrate diverse diagnostic factors, providing a comprehensive and accurate assessment that surpasses traditional methods.

Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to determine two positional relationships (true contact and bucco-lingual position) between M3 and IAN when they were overlapped in panoramic radiographs and compare its performance with that of oral and maxillofacial surgery (OMFS) specialists. A total of 571 panoramic images of M3 from 394 patients was used for this study. Among the images, 202 were classified as true contact, 246 as intimate, 61 as IAN buccal position, and 62 as IAN lingual position. A deep convolutional neural network model with ResNet-50 architecture was trained for each task. We randomly split the dataset into 75% for training and validation and 25% for testing. Model performance was superior in bucco-lingual position determination (accuracy 0.76, precision 0.83, recall 0.67, and F1 score 0.73) to true contact position determination (accuracy 0.63, precision 0.62, recall 0.63, and F1 score 0.61). AI exhibited much higher accuracy in both position determinations compared to OMFS specialists. In determining true contact position, OMFS specialists demonstrated an accuracy of 52.68% to 69.64%, while the AI showed an accuracy of 72.32%. In determining bucco-lingual position, OMFS specialists showed an accuracy of 32.26% to 48.39%, and the AI showed an accuracy of 80.65%. Moreover, Cohen’s kappa exhibited a substantial level of agreement for the AI (0.61) and poor agreements for OMFS specialists in bucco-lingual position determination. Determining the position relationship between M3 and IAN is possible using AI, especially in bucco-lingual positioning. The model could be used to support clinicians in the decision-making process for M3 treatment.

Mandibular fractures are among the most frequent facial traumas in oral and maxillofacial surgery, accounting for 57% of cases. An accurate diagnosis and appropriate treatment plan are vital in achieving optimal re-establishment of occlusion, function and facial aesthetics. This study aims to detect mandibular fractures on panoramic radiographs (PR) automatically. 1624 PR with fractures were manually annotated and labelled as a reference. A deep learning approach based on Faster R-CNN and Swin-Transformer was trained and validated on 1640 PR with and without fractures. Subsequently, the trained algorithm was applied to a test set consisting of 149 PR with and 171 PR without fractures. The detection accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an F1 score of 0.947 and an AUC of 0.977. Deep learning-based assistance of clinicians may reduce the misdiagnosis and hence the severe complications.