Fully Automated Deep Learning Model for Detecting Proximity of Mandibular Third Molar Root to Inferior Alveolar Canal using Panoramic Radiographs

Abstract

Objective

This study endeavored to develop a novel fully-automated deep learning model to determine the topographic relationship between mandibular third molar (MM3) roots and inferior alveolar canal (IAC) using panoramic radiographs (PR).

Study Design

A total of 1570 eligible patients with MM3s who had paired PR and cone-beam computed tomography (CBCT) from January 2019 to December 2020 were retrospectively collected and randomly grouped into training (80%), validation (10%), and testing (10%) cohorts. Spatial relationship of MM3/IAC was assessed by CBCT and set as the ground truth. MM3-IACnet, a modified deep learning network based on YOLOv5 (You only look once) was trained to detect MM3/IAC proximity using PR. Its diagnostic performance was further compared with dentists, AlexNet, GoogleNet, VGG-16, ResNet-50, and YOLOv5 in another independent cohort with 100 high-risk MM3 defined as root overlapping with IAC on PR.

Results

The MM3-IACnet performed best in predicting the MM3/IAC proximity as evidenced by the highest accuracy (0.885), precision (0.899), AUC value (0.95) and minimal time-spending compared to other models. Moreover, our MM3-IACnet outperformed other models in MM3/IAC risk prediction in high-risk cases.

Conclusion

MM3-IACnet model can assist clinicians in MM3s risk assessment and treatment planning by detecting MM3/IAC topographic relationship using PR.