Fully automated deep learning model for detecting proximity of mandibular third molar root to inferior alveolar canal using panoramic radiographs

Qiuping Jing; Xiubin Dai; Zhifan Wang; Yanqi Zhou; Yijin Shi; Shengjun Yang; Dongmiao Wang

doi:10.1016/j.oooo.2024.02.011

Fully automated deep learning model for detecting proximity of mandibular third molar root to inferior alveolar canal using panoramic radiographs

Oral Surg Oral Med Oral Pathol Oral Radiol. 2024 Feb 20:S2212-4403(24)00067-1. doi: 10.1016/j.oooo.2024.02.011. Online ahead of print.

Authors

Qiuping Jing¹, Xiubin Dai², Zhifan Wang¹, Yanqi Zhou², Yijin Shi¹, Shengjun Yang¹, Dongmiao Wang³

Affiliations

¹ Department of Oral and Maxillofacial Surgery, Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, China PRC; Jiangsu Province Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, China PRC.
² School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, China; Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, China.
³ Department of Oral and Maxillofacial Surgery, Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, China PRC; Jiangsu Province Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, China PRC; Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Jiangsu, China PRC. Electronic address: Wdm9921@njmu.edu.cn.

PMID: 38614873
DOI: 10.1016/j.oooo.2024.02.011

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 the inferior alveolar canal (IAC) using panoramic radiographs (PRs).

Study design: A total of 1570 eligible subjects 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. The 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), area under the curve value (0.95), and minimal time-spending compared with 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.