Semi-supervised classification of dental conditions in panoramic radiographs using large language model and instance segmentation: A real-world dataset evaluation

Imaging modalities like X-rays, computerized tomography scans, and magnetic resonance imaging provide detailed views of teeth, bones, and soft tissues (White and Pharoah, 2014). These tools enhance the precision of diagnoses and treatments, ensuring better patient outcomes. Among the current imaging exams, radiographs are the most common in dentistry (White and Pharoah, 2014; Langlais and Miller, 2016), being requested to identify various pathologies like cavities, periodontal disease, impacted teeth, and bone infections (Chang et al., 2020; Yüksel et al., 2021) and track the progress of dental treatments. One of the most commonly used radiographs in dentistry is the panoramic radiograph (White and Pharoah, 2014; Langlais and Miller, 2016; Silva et al., 2018), which is an extraoral imaging technique where the X-ray film or sensor remains outside the patient's mouth during acquisition. In a single image, the panoramic radiograph provides a comprehensive view of both upper and lower jaws, but with less detail of the mouth structures (Haring and Jansen, 2000; Silva et al., 2018; Jader et al., 2018; Pinheiro et al., 2021). Figure 1 depicts an example of a panoramic radiograph, revealing the structures and their overlaps, which can lead to cluttered readings.