Evaluation of the Position of Impacted Maxillary Canines on Panoramic Radiographs Using Deep Learning with CBCT as the Reference Standard

Background/Objectives: This study aimed to detect impacted maxillary canines on panoramic radiographs using deep learning models, to classify their bucco-palatal position (buccal, mid-alveolar, palatal), to identify root resorption of adjacent teeth, and to classify the gamma angle according to the 65° threshold. Methods: A total of 683 panoramic radiographs containing impacted maxillary canines were retrospectively included, with cone-beam computed tomography used as the reference standard. For each of four predefined groups, the data were divided at the patient level into training (80%), validation (10%), and test (10%) sets, and an independent YOLOv11x-seg model was trained. Results: For impacted canine detection (Group I), the F1 score was 0.98. For positional classification (Group II), the highest performance was observed in the palatal position (F1 = 0.81; precision = 0.78; recall = 0.85), whereas lower performance was observed for the buccal and mid-alveolar positions (F1 = 0.59 for both). For root resorption (Group III), only one of nine resorption-positive teeth was correctly identified, resulting in an F1 score of 0.15 for the “resorption present” class and 0.73 for the “resorption absent” class. For gamma angle classification (Group IV), F1 scores were 0.70 above 65° and 0.77 below 65°. The critical success index values were 0.95, 0.51, 0.55, and 0.59 for Groups I–IV, respectively. Conclusions: The YOLOv11x-seg models showed promising performance for the detection and segmentation of impacted maxillary canines within the selected study population; however, their performance was limited for positional assessment and inadequate for reliable detection of root resorption on panoramic radiographs.