PDF(Pubmed)
Abstract：
In the field of dentistry, the presence of dental calculus is a commonly encountered issue. If not addressed promptly, it has the potential to lead to gum inflammation and eventual tooth loss. Bitewing (BW) images play a crucial role by providing a comprehensive visual representation of the tooth structure, allowing dentists to examine hard-to-reach areas with precision during clinical assessments. This visual aid significantly aids in the early detection of calculus, facilitating timely interventions and improving overall outcomes for patients. This study introduces a system designed for the detection of dental calculus in BW images, leveraging the power of YOLOv8 to identify individual teeth accurately. This system boasts an impressive precision rate of 97.48%, a recall (sensitivity) of 96.81%, and a specificity rate of 98.25%. Furthermore, this study introduces a novel approach to enhancing interdental edges through an advanced image-enhancement algorithm. This algorithm combines the use of a median filter and bilateral filter to refine the accuracy of convolutional neural networks in classifying dental calculus. Before image enhancement, the accuracy achieved using GoogLeNet stands at 75.00%, which significantly improves to 96.11% post-enhancement. These results hold the potential for streamlining dental consultations, enhancing the overall efficiency of dental services.
摘要：
在牙科领域，牙结石的存在是一个常见的问题。如果不及时解决，它有可能导致牙龈发炎和最终的牙齿脱落。Bitewing（BW）图像通过提供牙齿结构的全面视觉表示来发挥关键作用，允许牙医在临床评估期间精确检查难以到达的区域。这种视觉辅助明显有助于早期发现结石，促进及时干预并改善患者的总体预后。这项研究介绍了一种设计用于BW图像中牙结石检测的系统，利用YOLOv8的力量准确识别单个牙齿。该系统拥有令人印象深刻的97.48％的准确率，召回率(敏感度)为96.81%，特异性率为98.25%。此外，这项研究介绍了一种新的方法来增强齿间边缘通过先进的图像增强算法。该算法结合了中值滤波器和双边滤波器的使用，以改善卷积神经网络在对牙结石进行分类时的准确性。在图像增强之前，使用GoogLeNet实现的准确度为75.00%，显着提高到增强后的96.11%。这些结果具有简化牙科咨询的潜力，提高牙科服务的整体效率。
