Role of impacted teeth in dental arch crowding

Estimates show that the prevalence of mandibular dental anterior crowding is high and might be up to 40%. The etiology of the condition has been multifactorial and evidence regarding the impact of mandibular third molars is still controversial. We discussed the potential role that impacted teeth (particularly mandibular third molars) might have in developing dental arch crowding. Evidence from different original studies and reviews regarding the impact of lower third molars on dental crowding was controversial. However, most of these studies showed that the correlation between these events was insignificant and additional studies might be needed for further validation. We have also identified many factors that can lead to dental arch crowding among the relevant studies in the literature. These factors might include general factors (including gender and age), skeletal factors (including malocclusion and growth of jaws) and dental factors (including primary tooth loss and tooth crown size), all of which were extensively discussed in the current study. Accordingly, further attention should also be paid to studying these factors.

Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars

Pell and Gregory, and Winter’s classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN) deep learning models using cropped panoramic radiographs based on these classifications. We compared the diagnostic accuracy of single-task and multi-task learning after labeling 1330 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014–2021). The mandibular third molar classifications were analyzed using a VGG 16 model of a CNN. We statistically evaluated performance metrics [accuracy, precision, recall, F1 score, and area under the curve (AUC)] for each prediction. We found that single-task learning was superior to multi-task learning (all p < 0.05) for all metrics, with large effect sizes and low p-values. Recall and F1 scores for position classification showed medium effect sizes in single and multi-task learning. To our knowledge, this is the first deep learning study to examine single-task and multi-task learning for the classification of mandibular third molars. Our results demonstrated the efficacy of implementing Pell and Gregory, and Winter’s classifications for specific respective tasks.

Automated Prediction of Extraction Difficulty and Inferior Alveolar Nerve Injury for Mandibular Third Molar Using a Deep Neural Network

Extraction of mandibular third molars is a common procedure in oral and maxillofacial surgery. There are studies that simultaneously predict the extraction difficulty of mandibular third molar and the complications that may occur. Thus, we propose a method of automatically detecting mandibular third molars in the panoramic radiographic images and predicting the extraction difficulty and likelihood of inferior alveolar nerve (IAN) injury. Our dataset consists of 4903 panoramic radiographic images acquired from various dental hospitals. Seven dentists annotated detection and classification labels. The detection model determines the mandibular third molar in the panoramic radiographic image. The region of interest (ROI) includes the detected mandibular third molar, adjacent teeth, and IAN, which is cropped in the panoramic radiographic image. The classification models use ROI as input to predict the extraction difficulty and likelihood of IAN injury. The achieved detection performance was 99.0% mAP over the intersection of union (IOU) 0.5. In addition, we achieved an 83.5% accuracy for the prediction of extraction difficulty and an 81.1% accuracy for the prediction of the likelihood of IAN injury. We demonstrated that a deep learning method can support the diagnosis for extracting the mandibular third molar.

Effects of Selective Alveolar Decortication on the Periodontal Complex

Background Modification of bone turnover has been reported following selective alveolar decortication but the molecular signals in the periodontal ligament space (PDL) remain unanswered. The objective of this study was to understand how selective alveolar decortication affects the biological reactions in the periodontal ligament. Methods Selective alveolar decortication in wild-type mice (n=25) was performed on mandibular right buccal cortical plate adjacent to the mandibular right third molar and euthanized at 3, 7, 14 and 28 days. We also performed selective alveolar decortication in Lrp5ACT (n=5) mice and Ad-Dkk1 treated mice (n=5), and euthanized at 7 days. The periodontium around the mandibular third molars were examined using histology, immunohistochemical analyses for osteogenic markers, TGF-β, RANKL, TRAP and alkaline phosphatase activity. Results The expression of osteogenic markers in the wild-type PDL was maintained during healing time period after selective alveolar decortication. Increased osteoclast activity in the wild-type mice was observed at 3 and 7 days after selective alveolar decortication. The PDL in Lrp5G171V (Lrp5ACT) mice and adenovirus Dkk1 (Ad-Dkk1) treated mice also showed insignificant changes in the expression of osteogenic markers following selective alveolar decortication. In Lrp5ACT mice where there was a reduction of bone resorption, selective alveolar decortication caused a dramatic increase in osteoclast activity. Conclusions Selective alveolar decortication affects only bone turnover, but not the expression of osteogenic markers in the PDL.

The Growth Factors in Advanced Platelet-Rich Fibrin (A-PRF) Reduce Postoperative Complications after Mandibular Third Molar Odontectomy

Surgical removal of impacted mandibular third molars constitutes one of the most frequently performed procedures within oral surgery. This surgery procedure is associated with many post-operative complications. Advanced platelet-rich fibrin (A-PRF) belongs to the second generation of platelet concentrates and is rich in numerous growth factors. The aim of this study was to assess the influence of A-PRF on selected clinical features following the surgical removal of impacted mandibular third molars. The research was conducted on 100 generally healthy patients, who underwent a lower third molar odontectomy in Department of Oral Surgery, Medical University of Gdańsk, Poland, between 2018 and 2019. The research group consisted of 50 patients (immediate A-PRF socket filling) and control group (50 patients without A-PRF socket filling). During the study, the following clinical features were assessed: pain (visual analog scale), analgesics intake, the presence of trismus, edema, hematomas within the surrounding tissues (e.g., cheek), prevalence of pyrexia, dry socket, secondary bleeding, presence of hematomas, skin warmth in the post-operative area, and bleeding time observed by the patient were analyzed on the 3rd, 7th, and 14th day after the procedure. There was a significant association between A-PRF socket filling and pain intensity, the analgesics intake, trismus, and edema on the 3rd and the 7th day (p < 0.05). The presence of hematomas and skin warmth on the 3rd day after the surgery (p < 0.05) were also statistically associated with A-PRF use. The study showed that in reducing the incidence of postoperative complications, A-PRF was more important than the position of the tooth or the duration of the procedure. The growth factors in A-PRF reduce postoperative complications, such as pain, trismus, edema, analgesics intake, presence of hematomas, and skin warmth, after mandibular wisdom teeth odontectomy.