Positive Predictive Value of Panoramic Radiography for Assessment of the Relationship of Impacted Mandibular Third Molars with the Mandibular Canal Based on Cone-Beam Computed Tomography: A Cross-Sectional Study

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.

Artificial Intelligence for Classifying the Relationship between Impacted Third Molar and Mandibular Canal on Panoramic Radiographs

The purpose of this investigation was to evaluate the diagnostic performance of two convolutional neural networks (CNNs), namely ResNet-152 and VGG-19, in analyzing, on panoramic images, the rapport that exists between the lower third molar (MM3) and the mandibular canal (MC), and to compare this performance with that of an inexperienced observer (a sixth year dental student). Utilizing the k-fold cross-validation technique, 142 MM3 images, cropped from 83 panoramic images, were split into 80% as training and validation data and 20% as test data. They were subsequently labeled by an experienced radiologist as the gold standard. In order to compare the diagnostic capabilities of CNN algorithms and the inexperienced observer, the diagnostic accuracy, sensitivity, specificity, and positive predictive value (PPV) were determined. ResNet-152 achieved a mean sensitivity, specificity, PPV, and accuracy, of 84.09%, 94.11%, 92.11%, and 88.86%, respectively. VGG-19 achieved 71.82%, 93.33%, 92.26%, and 85.28% regarding the aforementioned characteristics. The dental student's diagnostic performance was respectively 69.60%, 53.00%, 64.85%, and 62.53%. This work demonstrated the potential use of deep CNN architecture for the identification and evaluation of the contact between MM3 and MC in panoramic pictures. In addition, CNNs could be a useful tool to assist inexperienced observers in more accurately identifying contact relationships between MM3 and MC on panoramic images.