Third-Molar Status and Risk of Loss of Adjacent Second Molars

Efficacy of analgesia promoted by lidocaine and articaine in third molar extraction surgery. A split-mouth, randomized, controlled trial

PURPOSE

The aim of this study was to compare the analgesic efficacy of 4% articaine associated with epinephrine (1:100,000), and 2% lidocaine associated with epinephrine (1:100,000) in third molar extraction surgery.

METHODS

Sixty patients who underwent surgeries to extract upper and lower third molars were included in this split-mouth, double-blind, randomized, controlled trial. The groups in this study were divided according to the anesthetic solution used to provide local anesthesia during extraction of upper and lower third molars: (1) 4% articaine associated with epinephrine (1:100,000); (2) 2% lidocaine associated with epinephrine (1:100,000). The time to the beginning and end of the sensation of analgesia, pain sensation according to the VAS scale, and number of anesthetic tubes necessary for supplementation were analyzed.

RESULTS

It was found that the onset time for analgesia was shorter on the side anesthetized with articaine compared to the side anesthetized with lidocaine (122.1 ± 52.90 s vs. 144.5 ± 68.85 s) (p < 0.05). In addition, the number of tubes used for anesthetic supplementation was also reduced on the articaine side compared to the lidocaine side (0.26 ± 0.48 vs. 0.50 ± 0.75) (p < 0.05). There were no differences between the anesthetic solutions in the other evaluated parameters.

CONCLUSION

It can be concluded that the use of 4% articaine associated with epinephrine (1:100,000) reduced the time of onset of analgesia and the necessity for anesthetic supplementation in third molar extraction surgeries compared to the use of 2% lidocaine associated with epinephrine (1:100,000).

Non-Impacted Third Molars: Angels or Devils?

Third molars, also known as wisdom teeth, are located in the most posterior of the tooth arch [...].

How the Loss of Second Molars Corresponds with the Presence of Adjacent Third Molars in Chinese Adults: A Retrospective Study

Third molars (M3s) can increase the pathological risks of neighboring second molars (M2s). However, whether the M3 presence affects M2 loss remains unknown. This retrospective study aimed to reveal the reasons for M2 loss and how M2 loss relates to neighboring M3s. The medical records and radiographic images of patients with removed M2(s) were reviewed to analyze why the teeth were extracted and if those reasons were related to adjacent M3s. Ultimately, 800 patients with 908 removed M2s were included. In the included quadrants, 526 quadrants with M3s were termed the M3 (+) group, and the other 382 quadrants without M3s were termed the M3 (−) group. The average age of patients in the M3 (+) group was 52.4 ± 14.8 years and that of the M3 (−) group was 56.7 ± 14.9 years, and the difference between the two groups was statistically significant (p < 0.001). Of the 908 M2s, 433 (47.7%) were removed due to caries and sequelae and 300 (33.0%) were removed due to periodontal diseases. Meanwhile, 14.4% of the M2s with adjacent M3s were removed due to distal caries and periodontitis, which were closely related to the neighboring M3s; this percentage was much lower when M3 were absent (1.8%). Additionally, 42.2% of M3s were removed simultaneously with neighboring M2s. The presence of M3s, regardless of impaction status, was associated with an earlier loss of their neighboring M2s.

Prediction of post-traumatic neuropathy following impacted mandibular third molar removal

OBJECTIVES

The extraction of impacted mandibular third molars is a common surgical procedure often associated with complications including post-traumatic neuropathy. Previous work has focused on identifying confounding factors, but a robust preoperative risk prediction model remains elusive.

METHODS

Using a dataset of 648 patients and 812 impacted mandibular third molars, we used least absolute shrinkage and selection operator (LASSO) to fit prediction models based on risk factors assessed at both the tooth and patient levels. In addition, we fitted multivariable logistic regression models with the Firth correction for generalized estimating equations (GEE).

RESULTS

The LASSO model for post-traumatic neuropathy identified distoangular impaction of ≥ 45° (odds ratio [OR] = 2.9), proximity to the inferior alveolar nerve of ≤ 3 mm (OR = 1.9), disadvantageous curving (OR = 1.4), and psychiatric conditions (OR = 2.1) as predictors [area under the receiving operator characteristic curve (AUC) = 0.75]. Among other complications analyzed, the LASSO model for bleeding identified deep embedding or full impaction (OR = 1.8), psychiatric conditions (OR = 1.3), and age (OR = 0.9) as predictors (AUC = 0.64). These associations between predictors and postoperative complications were fundamentally reinforced by the corresponding GEE models.

CONCLUSIONS

Our findings point to the predictability of post-traumatic neuropathy and bleeding based on tooth anatomy and patient characteristics, overall suggesting that preoperatively identifiable factors can predict the risk of adverse outcomes in the extraction of impacted mandibular third molars.

CLINICAL SIGNIFICANCE

Mandibular third molar extraction is both a routine procedure and a leading cause of trigeminal neuropathy. Prevention of post-traumatic neuropathy, aided by individualized preoperative risk prediction, is of high clinical relevance.

Classification of caries in third molars on panoramic radiographs using deep learning

The objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped PR(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.86, a specificity of 0.88 and an AUC of 0.90 for the classification of carious lesions of third molars on PR(s). A high accuracy was achieved in caries classification in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future.