Huang Ya-Yun, Chen Chiung-An, Mao Yi-Cheng, Li Chih-Han, Li Bo-Wei, Chen Tsung-Yi, Tu Wei-Chen, Abu Patricia Angela R
Program on Semiconductor Manufacturing Technology Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City 701401, Taiwan.
Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan.
Diagnostics (Basel). 2025 Jul 2;15(13):1693. doi: 10.3390/diagnostics15131693.
In dental medicine, the integration of various types of X-ray images, such as periapical (PA), bitewing (BW), and panoramic (PANO) radiographs, is crucial for comprehensive oral health assessment. These complementary imaging modalities provide diverse diagnostic perspectives and support the early detection of oral diseases, thereby enhancing treatment outcomes. However, there is currently no existing system that integrates multiple types of dental X-rays for both adults and children to perform tooth localization and numbering. Therefore, this study aimed to propose a system based on YOLOv8 that integrates multiple dental X-ray images and automatically detects and numbers both permanent and deciduous teeth. Through image preprocessing, various types of dental X-ray images were standardized and enhanced to improve the recognition accuracy of individual teeth. With the implementation of a novel image preprocessing method, the system achieved a detection precision of 98.16% for permanent and deciduous teeth, representing a 3% improvement over models without image enhancement. In addition, the system attained an average tooth numbering accuracy of 98.5% for permanent teeth and 96.3% for deciduous teeth, surpassing existing methods by 5.6%. These results might highlight the innovation of the proposed image processing method and show its practical value in assisting clinicians with accurate diagnosis of tooth loss and the identification of missing teeth, ultimately contributing to improved diagnosis and treatment in dental care.
在牙科医学中,整合各种类型的X射线图像,如根尖片(PA)、咬合翼片(BW)和全景片(PANO),对于全面的口腔健康评估至关重要。这些互补的成像方式提供了多样的诊断视角,并支持口腔疾病的早期检测,从而提高治疗效果。然而,目前尚无现有的系统能够整合针对成人和儿童的多种类型牙科X射线,以进行牙齿定位和编号。因此,本研究旨在提出一种基于YOLOv8的系统,该系统整合多种牙科X射线图像,并自动检测恒牙和乳牙并进行编号。通过图像预处理,对各种类型的牙科X射线图像进行标准化和增强,以提高单个牙齿的识别准确率。通过实施一种新颖的图像预处理方法,该系统对恒牙和乳牙的检测精度达到了98.16%,比未进行图像增强的模型提高了3%。此外,该系统对恒牙的平均编号准确率为98.5%,对乳牙的平均编号准确率为96.3%,比现有方法高出5.6%。这些结果可能突出了所提出的图像处理方法的创新性,并显示了其在协助临床医生准确诊断牙齿缺失和识别缺失牙齿方面的实用价值,最终有助于改善牙科护理中的诊断和治疗。