Panoramic radiography is a fundamental diagnostic tool in dentistry, offering a comprehensive view of the entire dentition with minimal radiation exposure. However, manual interpretation is time-consuming and prone to errors, especially in high-volume clinical settings. This creates a pressing need for efficient automated solutions. This study presents the first application of YOLOv26 for automated tooth detection, FDI-based numbering, and dental disease segmentation in panoramic radiographs. The DENTEX dataset was preprocessed using Roboflow for format conversion and augmentation, yielding 1,082 images for tooth enumeration and 1,040 images for disease segmentation across four pathology classes. Five YOLOv26-seg variants were trained on Google Colab using transfer learning at a resolution of 800x800. Results demonstrate that the YOLOv26m-seg model achieved the best performance for tooth enumeration, with a precision of 0.976, recall of 0.970, and box mAP50 of 0.976. It outperformed the YOLOv8x baseline by 4.9% in precision and 3.3% in mAP50, while also enabling high-quality mask-level segmentation (mask mAP50 = 0.970). For disease segmentation, the YOLOv26l-seg model attained a box mAP50 of 0.591 and a mask mAP50 of 0.547. Impacted teeth showed the highest per-class average precision (0.943), indicating that visual distinctiveness influences detection performance more than annotation quantity. Overall, these findings demonstrate that YOLOv26-based models offer a robust and accurate framework for automated dental image analysis, with strong potential to enhance diagnostic efficiency and consistency in clinical practice.

翻译：全景X线摄影是牙科领域的一项基础诊断工具，能以最小辐射剂量提供全口牙列的综合影像。然而，人工判读耗时且易出错，尤其在接诊量大的临床场景中，这使得高效自动化解决方案的需求日益迫切。本研究首次将YOLOv26应用于全景X光片的自动化牙齿检测、FDI国际牙科编号系统标注及牙科疾病分割。利用Roboflow对DENTEX数据集进行格式转换与数据增强预处理，最终获得用于牙齿计数的1,082张图像和覆盖四类病理的1,040张疾病分割图像。基于800x800分辨率，在Google Colab平台采用迁移学习训练了五种YOLOv26-seg变体模型。结果表明，YOLOv26m-seg模型在牙齿计数任务中表现最优，精确率达0.976，召回率0.970，框mAP50达0.976，相较YOLOv8x基线模型精确度提升4.9%、mAP50提升3.3%，且同时实现了高质量掩膜级分割（掩膜mAP50=0.970）。在疾病分割任务中，YOLOv26l-seg模型取得框mAP50为0.591、掩膜mAP50为0.547的性能。阻生齿的每类平均精确率最高（0.943），表明视觉特征显著性对检测性能的影响程度超过标注样本数量。综上，本研究表明基于YOLOv26的模型为自动化牙科影像分析提供了鲁棒且精准的框架，具有显著提升临床诊断效率与一致性的潜力。