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.

Managing Curvatures in an Atypical Mandibular Third Molar: “Radix Paramolaris”

Third molars are known for morphological variations and atypical anatomy. Although these teeth pose difficulty in root canal treatment due to limited accessibility however, retaining third molars has gained importance in the present scenario due to their crucial role in serving as an abutment or in auto transplantation cases. This case report presents a case of Radix Paramolaris in mandibular third molar with severe curvatures of root canals in different planes and discusses its management with controlled memory files and tactile-controlled activation technique. Keywords: Radix Paramolaris, Curved canals, Controlled memory files, Tactile-controlled activation technique.

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.

Comparison of Two Different Doses of Dexamethasone to Control Swelling and Trismus following Mandibular Third Molar Surgery

Introduction: Third molar surgery is one of the most common procedures performed by dental surgeons in clinical practice, where postoperative sequelae like pain, trismus, and swelling are often encountered. Thus, in this study, we compared medications employed to reduce such complications. Objective: To compare the effect of two different doses (4 and 8 mg) of dexamethasone in the control of swelling and trismus after the surgical extraction of mandibular impacted third molars. Methods: An experimental study consisted of twenty-seven (27) healthy adult patients of both genders with bilateral impacted lower third molars, where surgical extraction was indicated, 4 mg and 8 mg of dexamethasone were given orally to the patients 1 hour before the surgical procedure at both the surgeries. The swelling was recorded by measuring the length of 3 facial planes using a measuring tape. Trismus was evaluated by measuring the maximum interincisal distance. Recording of facial swelling and maximum interincisal distance was done preoperatively and on the 1st and 2nd days postoperatively. Results: Based on statistical analysis (Independent T-test), the results showed a difference in the measurements of the degree of swelling and trismus of the treated sample. 8 mg of dexamethasone promoted a greater reduction of symptoms than 4mg of dexamethasone though there was no statistically significant (p-value >0.05) difference between the two doses. Conclusions: 8 milligrams of the dexamethasone had better effectiveness than 4 milligrams of the dexamethasone in reducing the degree of swelling and trismus. Keywords: Dexamethasone; impacted third molars; swelling; trismus.