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

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 and classified the lesions according to their depths and types for the provided samples. Accuracy, sensitivity, specificity, and F1 score values were measured for dental caries detection and classification. RESULTS: The diagnostic accuracy, sensitivity, specificity, and F1 score for caries detection in carious molar teeth were 95.3%, 92.1%, 96.3%, and 93.2%, respectively, and for noncarious molar teeth were 94.8%, 94.3%, 95.8%, and 94.6%. The CNN network showed high sensitivity, specificity, and accuracy in classifying caries extensions and locations. CONCLUSIONS: This research demonstrates that deep learning models can accurately identify dental caries and classify their depths and types with high accuracy, sensitivity, and specificity. The successful application of deep learning in this field will undoubtedly assist dental practitioners and patients in improving diagnostic and treatment planning in dentistry. CLINICAL SIGNIFICANCE: This study showed that deep learning can accurately detect and classify dental caries. Deep learning can provide dental caries detection accurately. Considering the shortage of dentists in certain areas, using CNNs can lead to broader geographic coverage in detecting dental caries.

A cloud-fog software architecture for dental CBCT dose monitoring using the DICOM structured report: Automated establishment of DRL.

PURPOSE: The diagnostic reference level (DRL) has been established to optimize the diagnostic methods and reduce radiation dose during radiographic examinations. The aim of this study was to present a completely new solution based on Cloud-Fog software architecture for automatic establishment of the DRL values during dental cone-beam computed tomography (CBCT) according to digital imaging and communications in medicine (DICOM) structured reports. METHODS AND MATERIALS: A Cloud-Fog software architecture was used for automatic data handling. This architecture used the DICOM structured reports as a source for extracting the required information by fog devices in the imaging center. These devices transferred the derived information to the cloud server. The cloud server calculated the value of indication-based DRL in dental CBCT imaging based upon the parameters and adequate quantities of the absorbed dose. The feedback of DRL value was continuously announced to the imaging centers in 6 phases. In each phase, the level of the dose was optimized in imaging centers. RESULTS: The DRL value was established for 5-specific indications, including third molar teeth (511 mGy.cm2), implant (719 mGy.cm2), form and position anomalies of the tooth (408 mGy.cm2), dentoalveolar pathologies (612 mGy.cm2), and endodontics (632 mGy.cm2). The determination of the DRL value in each phase revealed a downward trend until stabilization. CONCLUSION: The new solution presented in this study makes it possible to calculate and update the DRL value nationally and automatically among all centers. Also, the results showed that this approach is successful in establishing stabilized DRL values.

Sex classification of first molar teeth in cone beam computed tomography images using data mining.

OBJECTIVE: The teeth have been used as a supplementary tool for sex differentiation as they are resistant to post-mortem degradation. The present study aimed to develop a new novel informatics framework for predicting sex from linear tooth dimension measurements achieved from cone beam computed tomography (CBCT) images. METHOD AND MATERIALS: A clinical workflow using different machine learning methods was employed to predict the sex in the present study. The CBCT images of 485 subjects (245 men and 240 women) were evaluated for sex differentiation. Nine parameters were measured in both buccolingual and mesiodistal aspects of the teeth. We applied our dataset to Naïve Bayesian (NB), Random Forest (RF), and Support Vector Machine (SVM) as classifiers for prediction. Genetic feature selection was used to discover real features associated with sex classification. RESULTS: The 10-fold cross-validation results indicated that NB had higher accuracy than SVM and RF for sex classification. The genetic algorithm (GA) indicated that the model could fit the data without using the enamel thickness and pulp height. The average classification accuracy of our clinical workflow was 92.31 %. CONCLUSION: The results showed that NB was the best method for sex classification. The application of the first molar teeth in sex prediction indicated an acceptable level of sexual classification. Therefore, these odontometric parameters can be applied as an additional tool for sex determination in forensic anthropology.