In International journal of computerized dentistry
AIM : Although many studies in various fields employ deep learning models, only few such studies exist in dental imaging. This paper aims to evaluate the effectiveness of Convolutional Neural Network (CNN) algorithms for the detection and diagnosis of the quantitative level of dental restorations using panoramic radiographs by preparing a novel dataset.
MATERIALS AND METHODS : 20,973 panoramic X-ray radiographs, all labeled into five distinct categories by three dental experts, were used. AlexNet, VGG-16 and variants of ResNet models were trained with the dataset and evaluated for the classification task. Additionally, 10-fold cross-validation (i.e. nine-folds were separated for training and one-fold for validation) and data augmentation were carried out for all experiments.
RESULT : The most successful result came from ResNet-101 with 92.7% accuracy. Its macro-average AUC was also the highest with 0.989. Other accuracy results obtained for the dataset were 75.5% for AlexNet, 85.0% for VGG-16, 92.1% for ResNet-18, 91.7% for ResNet-50 and 92.1% for Inception ResNet V2.
CONCLUSION : Accuracy of 92.7% is a very promising result for a computer-aided diagnosis system. This result proved that the system could assist dentists in providing supportive preliminary information the moment they first take a panoramic X-ray radiograph of a patient. Furthermore, as the introduced dataset is powerful enough, it can be re-labeled for different problems and used in different studies.
Top Ahmet Esad, Özdoğan Sertaç, Yeniad Mustafa
2023-Jan-27
artificial intelligence, computer aided diagnosis, convolutional neural networks, deep learning, dental restoration, panoramic radiographs
