Anatomical Risk Factors of Inferior Alveolar Nerve Injury Association with Surgical Extraction of Mandibular Third Molar in Korean Population

Neurological Complications following Surgical Treatments of the Lower Molars

Aim

The current review aims to explore postoperative neurological complications in third molar extractive surgery.

Materials and Methods

The PRISMA protocols were followed when conducting this review. We found a total of 2,250 articles that matched our topic using the Boolean keywords, mandibular nerve complications AND oral surgery, from PubMed (1,083), Scopus (435), and Web of Science (732), with the filters of English language articles, time range January 1, 2003, to September 30, 2023, and human studies. After 762 duplicates were eliminated, there remained 1,488 articles. Eleven final articles were deemed of the highest relevance to our topic by eliminating articles in animals, non-English language, reviews, meta-analysis, and off-topic. A potential risk in the third molar extraction was temporary loss of sensibility often caused by mild compression or irritation of the mandibular nerve. This typically resolves within weeks or months, but in severe cases, recovery might take longer. Permanent loss of sensation can occur, indicating significant nerve damage and lasting effects on touch, temperature, or pain perception.

Conclusions

Various treatments exist for nerve damage, including low-level laser therapy, pain management medications, or physical therapy. While these therapies may improve neurosensory impairment, patients often report a decline in their quality of life.

Clinical Applications for Dentistry and Oral Health

In the last few years, dentistry has expanded the scope of its research and increased its cooperation with other disciplines [...]

A Fused Deep Learning Architecture for the Detection of the Relationship between the Mandibular Third Molar and the Mandibular Canal

The study aimed to generate a fused deep learning algorithm that detects and classifies the relationship between the mandibular third molar and mandibular canal on orthopantomographs. Radiographs (n = 1880) were randomly selected from the hospital archive. Two dentomaxillofacial radiologists annotated the data via MATLAB and classified them into four groups according to the overlap of the root of the mandibular third molar and mandibular canal. Each radiograph was segmented using a U-Net-like architecture. The segmented images were classified by AlexNet. Accuracy, the weighted intersection over union score, the dice coefficient, specificity, sensitivity, and area under curve metrics were used to quantify the performance of the models. Also, three dental practitioners were asked to classify the same test data, their success rate was assessed using the Intraclass Correlation Coefficient. The segmentation network achieved a global accuracy of 0.99 and a weighted intersection over union score of 0.98, average dice score overall images was 0.91. The classification network achieved an accuracy of 0.80, per class sensitivity of 0.74, 0.83, 0.86, 0.67, per class specificity of 0.92, 0.95, 0.88, 0.96 and AUC score of 0.85. The most successful dental practitioner achieved a success rate of 0.79. The fused segmentation and classification networks produced encouraging results. The final model achieved almost the same classification performance as dental practitioners. Better diagnostic accuracy of the combined artificial intelligence tools may help to improve the prediction of the risk factors, especially for recognizing such anatomical variations.

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 Digital OPG and CBCT in Assessment of Risk Factors Associated with Inferior Nerve Injury during Mandibular Third Molar Surgery

Background: Pre-operative radiographic assessment of the anatomical relationship between the roots of the mandibular third molar and the inferior alveolar nerve (IAN) is a must to minimize the risk of IAN injury during surgery. Objectives: To compare the radiographic signs of digital orthopantomogram (OPG) and cone-beam computed tomography (CBCT). An additional objective was to assess the cortex status between the mandibular canal and third molar on CBCT images in relation to the demographic characteristics, region (right or left side), and angulation of mandibular molar. Methodology: In this retrospective study, a total of 350 impacted mandibular third molars with a close relationship between the inferior alveolar canal (IAC) and impacted mandibular third molars on digital OPG were further referred for CBCT imaging for assessment of the position of the mandibular canal. The study was conducted between August 2018 and February 2020. Digital OPGs were evaluated for radiographic signs like interruption of the mandibular canal wall, darkening of the roots, diversion of the mandibular canal, and narrowing of the mandibular canal. The age and sex of patients, site of impacted third molar, Winter’s classification of mandibular third molar, position of IAC relative to impacted molar, and the radiographic markers of OPG were assessed for cortical integrity using CBCT. Chi square testing was applied to study the values of difference and binomial logistic regression was done to assess the factors associated with cortication. Statistical significance was set at p ≤ 0.05. Results: Among 350 patients, 207 (59.1%) were male and 143 (40.9%) were female with a mean age of 36.8 years. The most common OPG sign was interruption of white line, seen in 179 (51.1%) cases. In total, 246 cases (70.3%) showed an absence of canal cortication between the mandibular canal and the impacted third molar on CBCT images. Cortication was observed in all cases with a combination of panoramic signs which was statistically significant (p = 0.047). Cortication was observed in 85 (50.6%) cases where IAC was positioned on the buccal side, 11 (16.9%) in cases of inferiorly positioned IAC, and just 8 (7.6%) for cases of lingually positioned IAC which was statistically significant (p = 0.003). Statistically insignificant (p > 0.05) results were noted for cortex status in CBCT images with regards to the age, sex, site, and angulation of impacted third molars. Conclusion: CBCT imaging is highly recommended for those cases where diversion of the mandibular canal is observed on OPG and when the roots are present between canals.

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.