Correlation of Panoramic Radiological and Intra-Operative Findings of Impacted Mandibular 3rd Molar in Relation to Inferior Alveolar Canal: A Prospective Study

Radiographic Evaluation of Impacted Third Mandibular Molar According to the Classification of Winter, Pell and Gregory in a Sample of Cameroonian Population

Background

The extraction of impacted third molars (M3) is a common surgical procedure in dentistry and oral surgery. Various complications, including inferior alveolar nerve (IAN) damage, may occur during and after extraction of this tooth. Radiographic examination should provide information about the M3 itself, but also about the surrounding bony structure and the relationship of the roots to the IAN and the adjacent second molar, which is often traumatized during this extraction. The aim of our study was to evaluate the depth and angulation of impacted mandibular third molars (M3) from panoramic radiographs, according to the classifications proposed by Winter and Pell & Gregory.

Methods

Radiographic signs present on the orthopantomogram showing M3 depth, and retromandibular available space according to the Pell & Gregory classification were evaluated. Evaluation of the M3 angulation relative to the M2 according to Winter's classification was also done. Student's t test was used to determine the association between side or sex and different variables.

Results

The depth of impaction of the M3 crown was level A accounting for 54.4% (n=260) of the PR while level B constituted 35.7% (n=171) of the images. Regarding the availability of retromandibular space, Class I constituted 36.8% (n=176). The Class II accounted for 55.9% (n=267) of PR.

Conclusion

Our study showed that 54.4% of M3 were located at the same level as the occlusal plane of the second molar, while in 56% of PR the space between the second molar and the ramus of the mandible is less than the mesiodistal diameter of the third molar. This research showed that 23.1% of M3 had a level of vertical angulation, a level that allows for less painful luxation of the impacted molars. These results seem to show a relatively high level of difficulty in mobilizing and extracting M3 from Cameroonian patients.

Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography

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.