Deep learning-based evaluation of the relationship between mandibular third molar and mandibular canal on CBCT.
OBJECTIVES
The objective of our study was to develop and validate a deep learning approach based on convolutional neural networks (CNNs) for automatic detection of the mandibular third molar (M3) and the mandibular canal (MC) and evaluation of the relationship between them on CBCT.


MATERIALS AND METHODS
A dataset of 254 CBCT scans with annotations by radiologists was used for the training, the validation, and the test. The proposed approach consisted of two modules: (1) detection and pixel-wise segmentation of M3 and MC based on U-Nets; (2) M3-MC relation classification based on ResNet-34. The performances were evaluated with the test set. The classification performance of our approach was compared with two residents in oral and maxillofacial radiology.


RESULTS
For segmentation performance, the M3 had a mean Dice similarity coefficient (mDSC) of 0.9730 and a mean intersection over union (mIoU) of 0.9606; the MC had a mDSC of 0.9248 and a mIoU of 0.9003. The classification models achieved a mean sensitivity of 90.2%, a mean specificity of 95.0%, and a mean accuracy of 93.3%, which was on par with the residents.


CONCLUSIONS
Our approach based on CNNs demonstrated an encouraging performance for the automatic detection and evaluation of the M3 and MC on CBCT. Clinical relevance An automated approach based on CNNs for detection and evaluation of M3 and MC on CBCT has been established, which can be utilized to improve diagnostic efficiency and facilitate the precision diagnosis and treatment of M3.
['Liu|Mu-Qing|MQ|', 'Xu|Zi-Neng|ZN|', 'Mao|Wei-Yu|WY|', 'Li|Yuan|Y|', 'Zhang|Xiao-Han|XH|', 'Bai|Hai-Long|HL|', 'Ding|Peng|P|', 'Fu|Kai-Yuan|KY|http://orcid.org/0000-0001-7049-9888']
Mandibular third molar Mandibular canal CBCT Deep learning Convolutional neural networks