Prediction of Radiation-Related Dental Caries Through PyRadiomics Features and Artificial Neural Network on Panoramic Radiography

Abstract

The prediction and detection of radiation-related caries (RRC) are crucial to manage the side effects of the head and the neck cancer (HNC) radiotherapy (RT). Despite the demands for the prediction of RRC, no study proposes and evaluates a prediction method. This study introduces a method based on artificial intelligence neural network to predict and detect either regular caries or RRC in HNC patients under RT using features extracted from panoramic radiograph. We selected fifteen HNC patients (13 men and 2 women) to analyze, retrospectively, their panoramic dental images, including 420 teeth. Two dentists manually labeled the teeth to separate healthy and teeth with either type caries. They also labeled the teeth by resistant and vulnerable, as predictive labels telling about RT aftermath caries. We extracted 105 statistical/morphological image features of the teeth using PyRadiomics. Then, we used an artificial neural network classifier (ANN), firstly, to select the best features (using maximum weights) and then label the teeth: in caries and non-caries while detecting RRC, and resistant and vulnerable while predicting RRC. To evaluate the method, we calculated the confusion matrix, receiver operating characteristic (ROC), and area under curve (AUC), as well as a comparison with recent methods. The proposed method showed a sensibility to detect RRC of 98.8% (AUC = 0.9869) and to predict RRC achieved 99.2% (AUC = 0.9886). The proposed method to predict and detect RRC using neural network and PyRadiomics features showed a reliable accuracy able to perform before starting RT to decrease the side effects on susceptible teeth.

Keywords: Dental caries; Neural networks; Panoramic radiography; PyRadiomics features; Radiotherapy.

Fig. 1

The proposed pipeline as a sequence of steps of the image and data processing to detect and predict caries. ANN = artificial neural network classifier

Fig. 2

We defined an ANN with a hidden layer in the MATLAB software. The ANN input is the selected feature scores, and the output is a label set of 1 and 2 presenting dental caries vs healthy teeth or resistant teeth vs susceptible teeth. W = weights, b = bias

Fig. 3

Confusion matrix, a standard way to estimate a classifier’s performance in labeling data into two classes. True-positive (TP), false-negative (FN), false-positive (FP), true-negative (TN), sensitivity (ST). specificity (SF), positive predictive (PP), negative predictive (NP), and accuracy (AC)

Fig. 4

An example of A) original panoramic dental image, B) the first manual label map of healthy teeth (green) and dental caries (red) for detection approach, and C) the second manual label map of resistant (blue) and vulnerable (green) for prediction approach. In the second label map, caries dental before RT are excluded (gray)

Fig. 5

A) Confusion matrix., B) ROC calculated for the best results of ANN on all 420 teeth in detection approach, generated by MATLAB. Class1 for healthy and 2 for dental caries

Fig. 6

A) Confusion matrices. B) ROC of the best performance of ANN Classifier on vulnerable (class 1) and resistant (class 2) patient teeth (219) under radiotherapy treatment