Huang Ya-Yun, Mao Yi-Cheng, Chen Tsung-Yi, Chen Chiung-An, Chen Shih-Lun, Huang Yu-Jui, Chen Chun-Han, Chen Jun-Kai, 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 70101, Taiwan.
Department of General Dentistry, Chang Gung Memorial Hospital, Taoyuan City 33305, Taiwan.
Diagnostics (Basel). 2025 May 28;15(11):1363. doi: 10.3390/diagnostics15111363.
Panoramic radiography (PANO) is widely utilized for routine dental examinations, as a single PANO image captures most anatomical structures and clinical findings, enabling an initial assessment of overall dental health. Dentists rely on PANO images to enhance clinical diagnosis and inform treatment planning. With the advancement of artificial intelligence (AI), the integration of clinical data and AI-driven analysis presents significant potential for supporting medical applications. The proposed method focuses on the segmentation and localization of impacted third molars in PANO images, incorporating Sobel edge detection and enhancement methods to improve feature extraction. A convolutional neural network (CNN) was subsequently trained to develop an automated impacted tooth detection system. Experimental results demonstrated that the trained CNN achieved an accuracy of 84.48% without image preprocessing and enhancement. Following the application of the proposed preprocessing and enhancement methods, the detection accuracy improved significantly to 98.66%. This substantial increase confirmed the effectiveness of the image preprocessing and enhancement strategies proposed in this study. Compared to existing methods, which achieve approximately 90% accuracy, the proposed approach represents a notable improvement. Furthermore, the entire process, from inputting a raw PANO image to completing the detection, takes only 4.4 s. This system serves as a clinical decision support system for dentists and medical professionals, allowing them to focus more effectively on patient care and treatment planning.
全景X线摄影（PANO）被广泛用于常规牙科检查，因为一张PANO图像就能捕捉到大多数解剖结构和临床发现，从而能够对整体牙齿健康状况进行初步评估。牙医依靠PANO图像来加强临床诊断并为治疗计划提供依据。随着人工智能（AI）的发展，临床数据与AI驱动分析的整合在支持医疗应用方面具有巨大潜力。所提出的方法专注于PANO图像中阻生第三磨牙的分割和定位，结合Sobel边缘检测和增强方法来改进特征提取。随后训练了一个卷积神经网络（CNN）以开发一个自动阻生牙检测系统。实验结果表明，未经图像预处理和增强时，训练后的CNN准确率为84.48%。应用所提出的预处理和增强方法后，检测准确率显著提高到98.66%。这一显著提高证实了本研究中提出的图像预处理和增强策略的有效性。与现有方法约90%的准确率相比，所提出的方法有显著改进。此外，从输入原始PANO图像到完成检测的整个过程仅需4.4秒。该系统可作为牙医和医疗专业人员的临床决策支持系统，使他们能够更有效地专注于患者护理和治疗计划。
