Prediction of pulp exposure risk of carious pulpitis based on deep learning.
10.7518/gjkq.2023.2022418
- Author:
Li WANG
1
;
Fei WU
2
;
Mo XIAO
1
;
Yu-Xin CHEN
1
;
Ligeng WU
1
Author Information
1. Dept. of Endodontics, Stomatological Hospital, Tianjin Medical University, Tianjin 300070, China.
2. Dept. of General Dentistry, Yantai Stomatological Hospital Affiliated Binzhou Medical College, Yantai 264008, China.
- Publication Type:Journal Article
- Keywords:
caries-induced pulpitis;
convolution neural network;
deep learning;
dentinal caries
- MeSH:
Humans;
Deep Learning;
Neural Networks, Computer;
Pulpitis/diagnostic imaging*;
Reproducibility of Results;
ROC Curve;
Random Allocation
- From:
West China Journal of Stomatology
2023;41(2):218-224
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:This study aims to predict the risk of deep caries exposure in radiographic images based on the convolutional neural network model, compare the prediction results of the network model with those of senior dentists, evaluate the performance of the model for teaching and training stomatological students and young dentists, and assist dentists to clarify treatment plans and conduct good doctor-patient communication before surgery.
METHODS:A total of 206 cases of pulpitis caused by deep caries were selected from the Department of Stomatological Hospital of Tianjin Medical University from 2019 to 2022. According to the inclusion and exclusion criteria, 104 cases of pulpitis were exposed during the decaying preparation period and 102 cases of pulpitis were not exposed. The 206 radiographic images collected were randomly divided into three groups according to the proportion: 126 radiographic images in the training set, 40 radiographic images in the validation set, and 40 radiographic images in the test set. Three convolutional neural networks, visual geometry group network (VGG), residual network (ResNet), and dense convolutional network (DenseNet) were selected to analyze the rules of the radiographic images in the training set. The radiographic images of the validation set were used to adjust the super parameters of the network. Finally, 40 radiographic images of the test set were used to evaluate the performance of the three network models. A senior dentist specializing in dental pulp was selected to predict whether the deep caries of 40 radiographic images in the test set were exposed. The gold standard is whether the pulp is exposed after decaying the prepared hole during the clinical operation. The prediction effect of the three network models (VGG, ResNet, and DenseNet) and the senior dentist on the pulp exposure of 40 radiographic images in the test set were compared using receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score to select the best network model.
RESULTS:The best network model was DenseNet model, with AUC of 0.97. The AUC values of the ResNet model, VGG model, and the senior dentist were 0.89, 0.78, and 0.87, respectively. Accuracy was not statistically different between the senior dentist (0.850) and the DenseNet model (0.850)(P>0.05). Kappa consistency test showed moderate reliability (Kappa=0.6>0.4, P<0.05).
CONCLUSIONS:Among the three convolutional neural network models, the DenseNet model has the best predictive effect on whether deep caries are exposed in imaging. The predictive effect of this model is equivalent to the level of senior dentists specializing in dental pulp.
