METHODS: This study introduces a data-driven model for accurately diagnosing dental decay through the use of Bitewing radiology images using convolutional neural networks. The dataset utilized in this research includes 713 patient images obtained from the Samin Maxillofacial Radiology Center located in Tehran, Iran. The images were captured between June 2020 and January 2022 and underwent processing via four distinct Convolutional Neural Networks. The images were resized to 100 × 100 and then divided into two groups: 70% (4219) for training and 30% (1813) for testing. The four networks employed in this study were AlexNet, ResNet50, VGG16, and VGG19.
RESULTS: Among different well-known CNN architectures compared in this study, the VGG19 model was found to be the most accurate, with a 93.93% accuracy.
CONCLUSIONS: This promising result indicates the potential for developing an automatic AI-based dental caries diagnostic model from Bitewing images. It has the potential to serve patients or dentists as a mobile app or cloud-based diagnosis service (clinical decision support system).
方法:本研究介绍了一种数据驱动模型,用于通过使用卷积神经网络的Bitewing放射学图像来准确诊断龋齿。本研究中使用的数据集包括从位于德黑兰的Samin颌面放射学中心获得的713张患者图像,伊朗。这些图像是在2020年6月至2022年1月之间拍摄的,并通过四个不同的卷积神经网络进行处理。将图像大小调整为100×100,然后分为两组:70%(4219)用于训练,30%(1813)用于测试。这项研究中使用的四个网络是AlexNet,ResNet50、VGG16和VGG19。
结果:在本研究中比较的不同知名CNN架构中,VGG19模型被发现是最准确的,准确率为93.93%。
结论:这个有希望的结果表明,从Bitewing图像开发基于AI的自动龋齿诊断模型的潜力。它有可能作为移动应用程序或基于云的诊断服务(临床决策支持系统)为患者或牙医提供服务。