Automatic detection and proximity quantification of inferior alveolar nerve and mandibular third molar on cone-beam computed tomography.

Objectives: During mandibular third molar (MTM) extraction surgery, preoperative analysis to quantify the proximity of the MTM to the surrounding inferior alveolar nerve (IAN) is essential to minimize the risk of IAN injury. This study aims to propose an automated tool to quantitatively measure the proximity of IAN and MTM in cone-beam computed tomography (CBCT) images.
Materials and Methods: Using the dataset including 302 CBCT scans with 546 MTMs, a deep-learning-based network was developed to support the automatic detection of the IAN, MTM, and intersection region IR. To ensure accurate proximity detection, a distance detection algorithm and a volume measurement algorithm were also developed.
Results: The deep learning-based model showed encouraging segmentation accuracy of the target structures (Dice similarity coefficient: 0.9531 ± 0.0145, IAN; 0.9832 ± 0.0055, MTM; 0.8336 ± 0.0746, IR). In addition, with the application of the developed algorithms, the distance between the IAN and MTM and the volume of the IR could be equivalently detected (90% confidence interval (CI): - 0.0345-0.0014 mm, distance; - 0.0155-0.0759 mm ³ , volume). The total time for the IAN, MTM, and IR segmentation was 2.96 ± 0.11 s, while the accurate manual segmentation required 39.01 ± 5.89 min.
Conclusions: This study presented a novel, fast, and accurate model for the detection and proximity quantification of the IAN and MTM on CBCT.
Clinical Relevance: This model illustrates that a deep learning network may assist surgeons in evaluating the risk of MTM extraction surgery by detecting the proximity of the IAN and MTM at a quantitative level that was previously unparalleled.
Competing Interests: Declarations. Ethics approval: All experimental procedures involving patient radiographic data were approved by the West China Hospital Stomatology Ethics Committee (WCHSIRB-D-2021–331) of Chengdu, Sichuan, China. Informed consent was signed by each patient and at least one parent. Informed consent (consent to participate, consent to publish): Consent for publication was signed by each patient whose data was contained in the manuscript. Conflict of interest: The authors declare no competing interests.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)