AI system improves accuracy of CBCT images for implant placement, third molar extraction and orthognathic surgery: Study

A study was done to develop a deep learning-based system for precise, robust, and fully automated segmentation of the mandibular canal on cone beam computed tomography (CBCT) images. The system was developed on 536 CBCT scans (training set: 376, validation set: 80, testing set: 80) from one center and validated on an external dataset of 89 CBCT scans from 3 centers. Each scan was annotated using a multi-stage annotation method and refined by oral and maxillofacial radiologists. We proposed a three-step strategy for the mandibular canal segmentation: extraction of the region of interest based on 2D U-Net, global segmentation of the mandibular canal, and segmentation refinement based on 3D U-Net. Results: The system consistently achieved accurate mandibular canal segmentation in the internal set (Dice similarity coefficient [DSC], 0.952; intersection over union [IoU], 0.912; average symmetric surface distance [ASSD], 0.046 mm; 95% Hausdorff distance [HD95], 0.325 mm) and the external set (DSC, 0.960; IoU, 0.924; ASSD, 0.040 mm; HD95, 0.288 mm). These results demonstrated the potential clinical application of this AI system in facilitating clinical workflows related to mandibular canal localization. Accurate delineation of the mandibular canal on CBCT images is critical for implant placement, mandibular third molar extraction, and orthognathic surgery. This AI system enables accurate segmentation across different models, which could contribute to more efficient and precise dental automation systems.

Reference:

Fang-Duan Ni, Zi-Neng Xu, Mu-Qing Liu, Min-Juan Zhang, Shu Li, Hai-Long Bai, Peng Ding, Kai-Yuan Fu.Towards clinically applicable automated mandibular canal segmentation on CBCT, Journal of Dentistry, Volume 144, 2024, 104931, ISSN 0300-5712,

https://doi.org/10.1016/j.jdent.2024.104931.

(https://www.sciencedirect.com/science/article/pii/S0300571224001015)

Keywords:

Journal of Dentistry, AI system, improves, accuracy, CBCT images, implant placement, third molar extraction, orthognathic surgery, study, Fang-Duan Ni, Zi-Neng Xu, Mu-Qing Liu, Min-Juan Zhang, Shu Li, Hai-Long Bai, Peng Ding, Kai-Yuan Fu, Mandibular canal; Inferior alveolar nerve; Cone beam computed tomography; Deep learning; Convolutional neural networks