Dental Caries Detection and Classification in CBCT Images Using Deep Learning.

ABSTRACT:

Objectives

This study aimed to investigate the accuracy of deep learning algorithms to diagnose tooth caries and classify the extension and location of dental caries in cone beam computed tomography (CBCT) images. To the best of our knowledge, this is the first study to evaluate the application of deep learning for dental caries in CBCT images.

Methods

The CBCT image dataset comprised 382 molar teeth with caries and 403 noncarious molar cases. The dataset was divided into a development set for training and validation and test set. Three images were obtained for each case, including axial, sagittal, and coronal. The test dataset was provided to a multiple-input convolutional neural network (CNN). The network made predictions regarding the presence or absence of dental decay a

SUBMITTER: Esmaeilyfard R 

PROVIDER: S-EPMC10988262 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature