Bayrakdar Ibrahim Sevki, Bilgir Elif, Kuran Alican, Celik Ozer, Orhan Kaan
Int J Comput Dent. 2025 Apr 24;0(0):0. doi: 10.3290/j.ijcd.b6173229.
Panoramic radiography is a frequently utilized imaging technique in standard dental examinations and provides many advantages. In this context, studies have been conducted to develop tools to assist physicians in clinical practice by using deep learning models to interpret panoramic radiography images. However, studies in the existing literature have generally addressed these conditions separately and studies that develop a multiclass diagnostic charting model that can detect and segment all these conditions are very limited. Therefore, the aim of this study to develop a deep learning model that can accurately evaluate and segment various dental issues and anatomical structures in panoramic radiographs obtained from different radiography devices and settings.
Panoramic radiographs were labelled for 33 different conditions in the categories of dental problems, dental restorations, dental implants, anatomical landmarks, periodontal conditions, jaw pathologies and periapical lesions. A YOLO-v8 model was employed to develop an artificial intelligence model for each labelling. A confusion matrix was utilised to successfully evaluate the developed models.
The algorithm achieved a precision value of 0.99-1 in accurately detecting various dental features, such as adult tooth numbering, filling, dental implants, dental pulp, root canal filling, mandibular canal, mandibular condyle, mandible, and pharyngeal airway. With respect to sensitivity, the adult tooth numbering, dental implants, mandibular canal, maxillary sinus, mandibular condyle, angulus mandible, nasal septum, mandible, and hard palate showed the highest values of 0.99-1. The F1-score reached the highest value of 0.99-1 for the root canal filling, adult tooth numbering, dental implants, mandibular canal, mandibular condyle, angulus mandible, mandible, and pharyngeal airway.
Artificial intelligence based on convolutional neural networks has a remarkable ability to detect different conditions observed in regular clinical evaluations in panoramic radiographs, displaying excellent performance. Based on these findings, it can be confidently stated that deep learning-based models has great potential to improve routine clinical practices for physicians.
全景放射摄影是标准牙科检查中常用的成像技术，具有诸多优势。在此背景下，已有研究通过使用深度学习模型解读全景放射摄影图像来开发辅助临床医生的工具。然而，现有文献中的研究通常分别处理这些情况，而开发能够检测和分割所有这些情况的多类诊断图表模型的研究非常有限。因此，本研究的目的是开发一种深度学习模型，该模型能够准确评估和分割从不同放射摄影设备和设置获得的全景X光片中的各种牙齿问题和解剖结构。
全景X光片针对牙齿问题、牙齿修复、牙种植体、解剖标志、牙周状况、颌骨病变和根尖病变等类别中的33种不同情况进行了标注。采用YOLO-v8模型为每个标注开发人工智能模型。利用混淆矩阵成功评估了所开发的模型。
该算法在准确检测各种牙齿特征方面，如恒牙编号、补牙、牙种植体、牙髓、根管充填、下颌管、下颌髁突、下颌骨和咽气道，精度值达到0.99 - 1。在敏感性方面，恒牙编号、牙种植体、下颌管、上颌窦、下颌髁突、下颌角、鼻中隔、下颌骨和硬腭的敏感性最高，为0.99 - 1。根管充填、恒牙编号、牙种植体、下颌管、下颌髁突、下颌角、下颌骨和咽气道的F1分数达到最高值0.99 - 1。
基于卷积神经网络的人工智能在检测全景X光片中常规临床评估中观察到的不同情况方面具有显著能力，表现出色。基于这些发现，可以有信心地说，基于深度学习的模型对改善医生的日常临床实践具有巨大潜力。
