Detection of the Pterygomaxillary Fissure on Panoramic Radiographs Using Deep Learning for Anatomical Landmark Identification

In this study, we evaluate the diagnostic performance of a U2-Net-based artificial intelligence (AI) model for identifying the pterygomaxillary fissure on dental panoramic radiographs and investigate its potential utility as a supportive tool for preliminary anatomical landmark identification and pre-surgical screening. A total of 270 panoramic radiographs showing at least one fully visible pterygomaxillary fissure were retrospectively selected. In these anonymized images, 501 pterygomaxillary fissures were identified and manually annotated by two independent examiners using CVAT v1.7.0 labeling software. On the test dataset, the segmentation model achieved a Dice coefficient of 0.904 (95% CI: 0.876–0.930) and an Intersection over Union (IoU) of 0.846 (95% CI: 0.810–0.879). Precision and recall values were 0.921 and 0.902, respectively, yielding an F1-score of 0.911. During training, the highest validation Dice coefficient reached 0.910, with a validation IoU of 0.844 and validation accuracy of 0.998. These findings demonstrate that the proposed model shows strong performance in accurately segmenting the pterygomaxillary fissure on panoramic radiographs and may serve as a supportive tool for preliminary anatomical landmark identification during initial anatomical assessment.