Current Research in Dental Sciences · Published 2026-02-25 · DOI 10.17567/currresdentsci.1488041
Objective: Winter classification (WC) is used in the radiographic evaluation of mandibular impacted third molars (MITM) before extraction. In our study, we investigated the classification performance of panoramic radiographs (PRs) using different versions of two convolutional networks (CNN).Methods: The analysis was performed using three different YOLO-v7 and five different YOLO-v8 CNN architectures for the WC of 716 MITM teeth in 532 PRs included in the dataset. The localization of the second and third molars on PR images was determined, and the diagnostic performance of WC in this area was measured. Precision, recall, and mean average precision (mAP) were statistically evaluated for each model.Results: For both architectures, the highest performance was obtained for horizontal classification, with a value of 0.917 for the mAP metric, while the lowest performance was found to be 0.799 for vertical classification. Looking at the mAP metric values for all classes in the study, YOLO-v8-m performed better than YOLO-v7, with a difference of 2.7%, resulting in an overall mAP of 0.838 for YOLO-v7 and 0.865 for YOLO-v8. For both YOLO-v7 and YOLO-v8, the mid-depth network performed better than the other sub-models.Conclusions: This is the first study in which WC is performed using YOLO-v7 and YOLO-v8 models. In line with our results, CNN models for WC in PR are promising. In future studies, better results can be obtained by increasing the dataset size, using images from different centers and developing CNN architectures. Thus, clinical use of artificial intelligence architectures may become widespread.Keywords: Convolutional neural network, object detection, panoramic radiography, third molar, winter
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