Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ordu University, Ordu, Turkey.
Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey.
Acta Odontol Scand. 2021 May;79(4):275-281. doi: 10.1080/00016357.2020.1840624. Epub 2020 Nov 11.
Radiological examination has an important place in dental practice, and it is frequently used in intraoral imaging. The correct numbering of teeth on radiographs is a routine practice that takes time for the dentist. This study aimed to propose an automatic detection system for the numbering of teeth in bitewing images using a faster Region-based Convolutional Neural Networks (R-CNN) method.
The study included 1125 bite-wing radiographs of patients who attended the Faculty of Dentistry of Ordu University from 2018 to 2019. A faster R-CNN an advanced object identification method was used to identify the teeth. The confusion matrix was used as a metric and to evaluate the success of the model.
The deep CNN system (CranioCatch, Eskisehir, Turkey) was used to detect and number teeth in bitewing radiographs. Of 715 teeth in 109 bite-wing images, 697 were correctly numbered in the test data set. The F1 score, precision and sensitivity were 0.9515, 0.9293 and 0.9748, respectively.
A CNN approach for the analysis of bitewing images shows promise for detecting and numbering teeth. This method can save dentists time by automatically preparing dental charts.
放射检查在口腔医学实践中占有重要地位，常被应用于口腔内影像学检查。正确标注 X 光片上的牙齿编号是牙医的常规操作，但很耗时。本研究旨在提出一种使用更快的基于区域的卷积神经网络（R-CNN）方法自动检测咬翼片图像中牙齿编号的系统。
本研究纳入了 2018 年至 2019 年期间在土耳其额尔祖鲁姆大学牙科学院就诊的 1125 例咬翼片 X 光片。使用更快的 R-CNN（一种先进的目标识别方法）来识别牙齿。混淆矩阵被用作衡量和评估模型成功的指标。
深度卷积神经网络系统（CranioCatch，埃斯基谢希尔，土耳其）被用于检测和标记咬翼片 X 光片中的牙齿。在 109 张咬翼片中的 715 颗牙齿中，有 697 颗在测试数据集被正确标记。F1 评分、准确率和敏感度分别为 0.9515、0.9293 和 0.9748。
用于分析咬翼片图像的 CNN 方法在检测和标记牙齿方面显示出了前景。这种方法可以通过自动生成牙科图表来为牙医节省时间。
