Multi-class anatomical landmark detection in periapical radiographs with deep learning.

This study aims to develop a deep learning model for the detection and segmentation of multiple anatomical landmarks on periapical radiographs from the maxilla and mandible. A total of 1930 paralleling-technique periapical radiographs with 21 annotated anatomical landmarks were divided into training (80%), validation (10%), and test (10%) sets. Geometry-preserving preprocessing was applied before dataset splitting, while appearance-based augmentation was performed exclusively on the training subset after the split. A YOLOv8x-seg architecture was trained for multi-class detection and instance segmentation. Performance was evaluated using precision, recall, F1-score, Dice coefficient, Intersection-over-Union, mean average precision, and receiver operating characteristic analysis. The model demonstrated stable training and consistent performance. Overall precision, recall, and F1-score were 0.820, 0.725, and 0.769, respectively, with an overall Dice coefficient of 0.621. High detection accuracy was achieved for well-defined structures such as the maxillary sinus, nasal fossa, nasal fossa floor, and nasal septum, whereas low-contrast landmarks showed reduced performance. Confidence-dependent analysis indicated optimal performance at low confidence thresholds (approximately 0.05-0.10). In conclusion, the proposed model effectively detected major anatomical landmarks on periapical radiographs while demonstrating expected limitations for small or low-contrast structures. Despite substantial anatomical variability across maxillary and mandibular regions, anterior-posterior sites, and projection-dependent appearances of similar structures, these findings demonstrate that deep learning can reliably identify key anatomical landmarks, supporting safer, more consistent, and clinically meaningful radiographic interpretation in routine dental practice.