PDF(Pubmed)
Abstract：
BACKGROUND: Dental caries, also known as tooth decay, is a widespread and long-standing condition that affects people of all ages. This ailment is caused by bacteria that attach themselves to teeth and break down sugars, creating acid that gradually wears away at the tooth structure. Tooth discoloration, pain, and sensitivity to hot or cold foods and drinks are common symptoms of tooth decay. Although this condition is prevalent among all age groups, it is especially prevalent in children with baby teeth. Early diagnosis of dental caries is critical to preventing further decay and avoiding costly tooth repairs. Currently, dentists employ a time-consuming and repetitive process of manually marking tooth lesions after conducting radiographic exams. However, with the rapid development of artificial intelligence in medical imaging research, there is a chance to improve the accuracy and efficiency of dental diagnosis.
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).
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).
摘要：
背景：龋齿，也被称为蛀牙，是一种广泛而长期的疾病，影响所有年龄段的人。这种疾病是由附着在牙齿上并分解糖的细菌引起的，产生的酸在牙齿结构上逐渐磨损。牙齿变色,疼痛,对冷热食物和饮料敏感是蛀牙的常见症状。尽管这种情况在所有年龄段都很普遍，它在有乳牙的儿童中尤其普遍。龋齿的早期诊断对于防止进一步腐烂和避免昂贵的牙齿修复至关重要。目前,牙医在进行射线照相检查后采用耗时且重复的手动标记牙齿病变的过程。然而,随着人工智能在医学影像研究中的快速发展,有机会提高牙科诊断的准确性和效率。
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。
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。
