Tez Banu Çiçek, Güzel Yasin, Kızıltan Eliaçık Bahar Başak, Aydın Zafer
Department of Pediatric Dentistry, Faculty of Dentistry, Ankara Medipol University, Ankara 06050, Türkiye.
Department of Educational Sciences, Suleyman Demirel University, Isparta 32200, Türkiye.
Children (Basel). 2025 Apr 7;12(4):475. doi: 10.3390/children12040475.
BACKGROUND/OBJECTIVES: Dental plaque is a significant contributor to various prevalent oral health conditions, including caries, gingivitis, and periodontitis. Consequently, its detection and management are of paramount importance for maintaining oral health. Manual plaque assessment is time-consuming, error-prone, and particularly challenging in uncooperative pediatric patients. These limitations have encouraged researchers to seek faster, more reliable methods. Accordingly, this study aims to develop a deep learning model for detecting and segmenting plaque in young permanent teeth and to evaluate its diagnostic precision.
The dataset comprises 506 dental images from 31 patients aged between 8 and 13 years. Six state-of-the-art models were trained and evaluated using this dataset. The U-Net Transformer model, which yielded the best performance, was further compared against three experienced pediatric dentists for clinical feasibility using 35 randomly selected images from the test set. The clinical trial was registered on under the ID NCT06603233 (1 June 2023).
The Intersection over Union (IoU) score of the U-Net Transformer on the test set was measured as 0.7845, and the -values obtained from the three -tests conducted for comparison with dentists were found to be below 0.05. Compared with three experienced pediatric dentists, the deep learning model exhibited clinically superior performance in the detection and segmentation of dental plaque in young permanent teeth.
This finding highlights the potential of AI-driven technologies in enhancing the accuracy and reliability of dental plaque detection and segmentation in pediatric dentistry.
背景/目的：牙菌斑是导致多种常见口腔健康问题的重要因素，包括龋齿、牙龈炎和牙周炎。因此，牙菌斑的检测和管理对于维持口腔健康至关重要。手动菌斑评估耗时、容易出错，对于不合作的儿科患者尤其具有挑战性。这些局限性促使研究人员寻求更快、更可靠的方法。因此，本研究旨在开发一种深度学习模型，用于检测和分割年轻恒牙中的菌斑，并评估其诊断精度。
数据集包括来自31名年龄在8至13岁之间患者的506张牙科图像。使用该数据集对六个最先进的模型进行了训练和评估。使用测试集中随机选择的35张图像，将性能最佳的U-Net Transformer模型与三位经验丰富的儿科牙医进行了临床可行性的进一步比较。该临床试验已在ID NCT06603233（2023年6月1日）下注册。
测试集中U-Net Transformer的交并比（IoU）分数为0.7845，与牙医比较进行的三次测试获得的p值均低于0.05。与三位经验丰富的儿科牙医相比，深度学习模型在年轻恒牙牙菌斑的检测和分割方面表现出临床上更优的性能。
这一发现凸显了人工智能驱动技术在提高儿科牙科中牙菌斑检测和分割的准确性和可靠性方面的潜力。
