Dental Panoramic Radiograph Analysis Using YOLO26 From Tooth Detection to Disease Diagnosis
2026-04-17 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors used a computer program called YOLOv26 to automatically identify and number teeth, as well as spot dental diseases in panoramic x-ray images. They trained their models using a special dataset with images labeled for teeth and different dental problems. Their best model was very accurate at finding and numbering teeth and could also outline dental issues reasonably well. Notably, teeth that look visually distinct, like impacted teeth, were easier for the model to detect. Overall, the authors showed that their approach could help dentists by making image analysis faster and more reliable.
Panoramic radiographyYOLOv26Dental image segmentationTooth enumerationFDI numbering systemTransfer learningPrecision and recallMean average precision (mAP)RoboflowDental pathology
Authors
Khawaja Azfar Asif, Rafaqat Alam Khan
Abstract
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.