Evaluation of the effectiveness of panoramic radiography in impacted mandibular third molars on deep learning models developed with findings obtained with cone beam computed tomography.

OBJECTIVE: The aim of this study is to determine the contact relationship and position of impacted mandibular third molar teeth (IMM(3)) with the mandibular canal (MC) in panoramic radiography (PR) images using deep learning (DL) models trained with the help of cone beam computed tomography (CBCT) and DL to compare the performances of the architectures. METHODS: In this study, a total of 546 IMM(3)s from 290 patients with CBCT and PR images were included. The performances of SqueezeNet, GoogLeNet, and Inception-v3 architectures in solving four problems on two different regions of interest (RoI) were evaluated. RESULTS: The SqueezeNet architecture performed the best on the vertical RoI, showing 93.2% accuracy in the identification of the 2nd problem (contact relationship buccal or lingual). Inception-v3 showed the highest performance with 84.8% accuracy in horizontal RoI for the 1st problem (contact relationship-no contact relationship), GoogLeNet showed 77.4% accuracy in horizontal RoI for the 4th problem (contact relationship buccal, lingual, other category, or no contact relationship), and GoogLeNet showed 70.0% accuracy in horizontal RoI for the 3rd problem (contact relationship buccal, lingual, or other category). CONCLUSION: This study found that the Inception-v3 model showed the highest accuracy values in determining the contact relationship, and SqueezeNet architecture showed the highest accuracy values in determining the position of IMM(3) relative to MC in the presence of a contact relationship.