External validation of cone-beam computed tomography- and panoramic radiography-featured prediction models for inferior alveolar nerve injury after lower third molar removal: proposal of a risk calculator

Asymmetric outcomes in bilateral maxillary impacted tooth extractions: A case report

BACKGROUND

The extraction of maxillary impacted teeth is a common procedure in oral surgery, frequently complicated by oroantral communications. For less-experienced clinicians, accurately assessing the difficulty and associated risks of maxillary third molar extractions remain a significant challenge.

CASE SUMMARY

We present a case involving disparate outcomes following bilateral extraction of maxillary third molars. Using cone-beam computed tomography and three-dimensional software, we conducted a digital assessment of the factors contributing to extraction difficulty and risk, controlling for potential confounders. Key variables analyzed included alveolar bone volume, bone quality, crown-root angulation, and maxillary sinus mucosal thickness. Additionally, we introduce the novel concept of "tegmen bone" to quantitatively evaluate the bone mass between the teeth and the maxillary sinus. This unique case, with differing outcomes on opposite sides of the same patient, provided an opportunity to minimize extraneous variables and focus on the local anatomical factors influencing the procedures, thereby improving the precision of our analysis.

CONCLUSION

This case highlights the potential utility of predictive analysis in guiding the management of complex tooth extractions.

Risk factors for impacted lower third molar root perforation through the undercut-shaped lingual plate: preoperative insights from panoramic radiography

This study aimed to identify risk factors associated with perforation of the undercut (U)-shaped lingual plate (LP) by the lower third molar (LM3) root using panoramic radiography (PAN). We retrospectively examined 468 impacted LM3s from 468 individuals, categorizing LM3-LP associations and LP morphology in the coronal section of cone-beam computed tomography as perforation or nonperforation and U-type or non-U-type, respectively. The outcome was the combination of perforation and U-type, and study variables included patient demographics (age and sex) and PAN-associated features (Winter's classification, Pell-Gregory classification, and two major Rood signs). Multivariate logistic regression methods were used for analysis. Perforated and U-type LPs were observed in 205 (43.8%) and 212 (45.3%) cases, respectively. The double-positive outcome was observed in 126 LM3s (26.9%). In the multivariate model, age ≥ 26 years [odds ratio (OR), 2.66; p = 0.002], men (OR, 2.01; p = 0.002), mesioangular (OR, 2.74; p = 0.038) and horizontal impaction (OR, 3.05; p = 0.019), and root darkening (OR, 1.73; p = 0.039) were independently associated with the risk. Class III impaction (OR, 0.35; p = 0.021) and interruption of the white line (OR, 0.55; p = 0.017) were negatively correlated with the risk. In conclusion, this study highlights the importance of identifying the higher probability of U-type LP perforation by the LM3 root in men aged over their midtwenties with Class I/II impaction and mesioangularly or horizontally impacted LM3s, along with root darkening and no interruption of the white line on PAN.

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.