Savaştaer Ertuğrul Furkan, Çelik Berrin, Çelik Mahmut Emin
Electrical Electronics Engineering Department, Faculty of Engineering, Gazi University, Ankara, Turkey.
Oral and Maxillofacial Radiology Department, Faculty of Dentistry, Ankara Yıldırım Beyazıt University, Ankara, Turkey.
BMC Oral Health. 2025 Apr 1;25(1):465. doi: 10.1186/s12903-025-05827-4.
The aim was to fully automate molar teeth developmental staging and to comprehensively analyze a wide range of deep learning models' performances for molar tooth germ detection on panoramic radiographs.
The dataset consisted of 210 panoramic radiographies. The data were obtained from patients aged between 5 and 25 years. The stages of development of molar teeth were divided into 4 classes such as M1, M2, M3 and M4. 9 different convolutional neural network models, which were Cascade R-CNN, YOLOv3, Hybrid Task Cascade(HTC), DetectorRS, SSD, EfficientNet, NAS-FPN, Deformable DETR and Probabilistic Anchor Assignment(PAA), were used for automatic detection of these classes. Performances were evaluated by mAP for detection localization performance and confusion matrices, giving metrics of accuracy, precision, recall and F1-scores for classification part.
Localization performance of the models varied between 0.70 and 0.86 while average accuracy for all classes was between 0.71 and 0.82. The Deformable DETR model provided the best performance with mAP, accuracy, recall and F1-score as 0.86, 0.82, 0.86 and 0.86 respectively.
Molar teeth were automatically detected and categorized by modern artificial intelligence techniques. Findings demonstrated that detection and classification ability of deep learning models were promising for molar teeth development staging. Automated systems have a potential to alleviate the burden and assist dentists.
This is retrospectively registered with the number 2023-1216 by the university ethical committee.
目的是实现磨牙发育分期的完全自动化，并全面分析多种深度学习模型在全景X线片上检测磨牙胚的性能。
数据集由210张全景X线片组成。数据来自5至25岁的患者。磨牙的发育阶段分为M1、M2、M3和M4四类。使用9种不同的卷积神经网络模型，即级联R-CNN、YOLOv3、混合任务级联（HTC）、DetectorRS、SSD、EfficientNet、NAS-FPN、可变形DETR和概率锚点分配（PAA），对这些类别进行自动检测。通过平均精度均值（mAP）评估检测定位性能，并通过混淆矩阵评估性能，给出分类部分的准确率、精确率、召回率和F1分数指标。
模型的定位性能在0.70至0.86之间，而所有类别的平均准确率在0.71至0.82之间。可变形DETR模型表现最佳，其mAP、准确率、召回率和F1分数分别为0.86、0.82、0.86和0.86。
通过现代人工智能技术自动检测并分类磨牙。研究结果表明，深度学习模型的检测和分类能力在磨牙发育分期方面前景广阔。自动化系统有减轻负担并辅助牙医的潜力。
本研究由大学伦理委员会进行回顾性注册，注册号为2023 - 1216。
