Şevik Uğur, Mutlu Onur
Department of Computer Science, Faculty of Science, Karadeniz Technical University, Kanuni Campus, 61080 Trabzon, Turkey.
Retina R&D Software and Engineering Services Ltd., Trabzon Teknokent, 61080 Trabzon, Turkey.
Diagnostics (Basel). 2025 Aug 5;15(15):1961. doi: 10.3390/diagnostics15151961.
: The diagnosis of pediatric dental conditions from panoramic radiographs is uniquely challenging due to the dynamic nature of the mixed dentition phase, which can lead to subjective and inconsistent interpretations. This study aims to develop and rigorously validate an advanced deep learning model to enhance diagnostic accuracy and efficiency in pediatric dentistry, providing an objective tool to support clinical decision-making. : An initial comparative study of four state-of-the-art YOLO variants (YOLOv8, v9, v10, and v11) was conducted to identify the optimal architecture for detecting four common findings: Dental Caries, Deciduous Tooth, Root Canal Treatment, and Pulpotomy. A stringent two-tiered validation strategy was employed: a primary public dataset ( = 644 images) was used for training and model selection, while a completely independent external dataset ( = 150 images) was used for final testing. All annotations were validated by a dual-expert team comprising a board-certified pediatric dentist and an experienced oral and maxillofacial radiologist. : Based on its leading performance on the internal validation set, YOLOv11x was selected as the optimal model, achieving a mean Average Precision (mAP50) of 0.91. When evaluated on the independent external test set, the model demonstrated robust generalization, achieving an overall F1-Score of 0.81 and a mAP50 of 0.82. It yielded clinically valuable recall rates for therapeutic interventions (Root Canal Treatment: 88%; Pulpotomy: 86%) and other conditions (Deciduous Tooth: 84%; Dental Caries: 79%). : Validated through a rigorous dual-dataset and dual-expert process, the YOLOv11x model demonstrates its potential as an accurate and reliable tool for automated detection in pediatric panoramic radiographs. This work suggests that such AI-driven systems can serve as valuable assistive tools for clinicians by supporting diagnostic workflows and contributing to the consistent detection of common dental findings in pediatric patients.
由于混合牙列期的动态特性，通过全景X光片诊断儿童牙齿疾病具有独特的挑战性，这可能导致主观且不一致的解读。本研究旨在开发并严格验证一种先进的深度学习模型，以提高儿童牙科的诊断准确性和效率，提供一种客观工具来支持临床决策。：对四种最先进的YOLO变体（YOLOv8、v9、v10和v11）进行了初步比较研究，以确定检测四种常见病症的最佳架构：龋齿、乳牙、根管治疗和牙髓切断术。采用了严格的两级验证策略：一个主要的公共数据集（=644张图像）用于训练和模型选择，而一个完全独立的外部数据集（=150张图像）用于最终测试。所有标注均由一个由获得委员会认证的儿童牙医和一位经验丰富的口腔颌面放射科医生组成的双专家团队进行验证。：基于其在内部验证集上的领先表现，YOLOv11x被选为最佳模型，平均精度均值（mAP50）为0.91。在独立外部测试集上进行评估时，该模型表现出强大的泛化能力，总体F1分数为0.81，mAP50为0.82。它在治疗干预（根管治疗：88%；牙髓切断术：86%）和其他病症（乳牙：84%；龋齿：79%）方面产生了具有临床价值的召回率。：通过严格的双数据集和双专家流程验证，YOLOv11x模型展示了其作为儿童全景X光片自动检测的准确可靠工具的潜力。这项工作表明，此类人工智能驱动的系统可以通过支持诊断工作流程并有助于一致地检测儿科患者的常见牙齿病症，为临床医生提供有价值的辅助工具。
