Healthcare costs of post-traumatic trigeminal neuropathy in Belgium - A retrospective analysis

Radiological assessment of the inferior alveolar canal and mental foramen using cone beam computed tomography for pre-operative evaluation of surgeries in the mandible: A single-center five-year retrospective study

Introduction

Cone beam computed tomography (CBCT) plays a significant role in studying the anatomical structures of the mandible.

Aim

This retrospective study aimed to assess the role of CBCT at the pre-assessment stage of mandibular surgery.

Materials and methods

A total of 250 CBCT images were collected. The inferior alveolar canal (IAC) and mental foramen (MF) were measured bilaterally to the mandibular teeth apexes, including molars, premolars, and canines, to the buccal, lingual cortical bone, and to the inferior border of the mandible.

Results

There were no differences in the average number of extracted teeth between the right and left sides (P-value > 0.05, median = three teeth). It was noticed that the average measures of each point of the IAC and MF on the right side were closely matched to the similar point on the left side (P-value > 0.05). T-tests showed that there were differences between males and females on the M2 and M3 on the right side (P-value < 0.05) and on the M1, M2, and M3 on the left side (P-value < 0.05). Using one-way ANOVA tests, results showed that there were some differences in measures at P0 (F = 3.376, P-value = 0.003), P4 (F = 3.782, P-value = 0.001) on the right side, and at P3 (F = 5.620, P-value = 0.019) on the left side of the mandible.

Conclusions

There were no significant differences in IAC and MF positions between the right and left sides. However, between males and females, MF measurements showed significant differences on some points on the right and left sides. Although the history of extracted teeth showed no statistically significant difference in the location of IAC and MF, the number of extracted teeth showed an effect in the IAC position on the right and left sides, but not with the MF.

Risk assessment of inferior alveolar nerve injury after wisdom tooth removal using 3D AI-driven models: A within-patient study

OBJECTIVE

To compare a three-dimensional (3D) artificial intelligence (AI)- driven model with panoramic radiography (PANO) and cone-beam computed tomography (CBCT) in assessing the risk of inferior alveolar nerve (IAN) injury after mandibular wisdom tooth (M3M) removal through a within-patient controlled trial.

METHODS

From a database of 6,010 patients undergoing M3M surgery, 25 patients met the inclusion criteria of bilateral M3M removal with postoperative unilateral IAN injury. In this within-patient controlled trial, preoperative PANO and CBCT images were available, while 3D-AI models of the mandibular canal and teeth were generated from the CBCT images using the Virtual Patient Creator AI platform (Relu BV, Leuven, Belgium). Five examiners, who were blinded to surgical outcomes, assessed the imaging modalities and assigned scores indicating the risk level of IAN injury (high, medium, or low risk). Sensitivity, specificity, and area under receiver operating curve (AUC) for IAN risk assessment were calculated for each imaging modality.

RESULTS

For IAN injury risk assessment after M3M removal, sensitivity was 0.87 for 3D-AI, 0.89 for CBCT versus 0.73 for PANO. Furthermore, the AUC and specificity values were 0.63 and 0.39 for 3D-AI, 0.58 and 0.28 for CBCT, and 0.57 and 0.41 for PANO, respectively. There was no statistically significant difference (p>0.05) among the imaging modalities for any diagnostic parameters.

CONCLUSION

This within-patient controlled trial study revealed that risk assessment for IAN injury after M3M removal was rather similar for 3D-AI, PANO, and CBCT, with a sensitivity for injury prediction reaching up to 0.87 for 3D-AI and 0.89 for CBCT.

CLINICAL SIGNIFICANCE

This within-patient trial is pioneering in exploring the application of 3D AI-driven models for assessing IAN injury risk after M3M removal. The present results indicate that AI-powered 3D models based on CBCT might facilitate IAN risk assessment of M3M removal.