Panognostics-Plus: An Integrated Multi-Task AI Framework for Automated Panoramic Radiograph Interpretation with Dental Charting and Structured Reporting-Development and Technical Validation Study.
Authors
Affiliations (10)
Affiliations (10)
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Catholic University Leuven, 3000, Leuven, Belgium.
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000, Leuven, Belgium.
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia.
- Department of Pathology, University Hospitals Leuven, 3000, Leuven, Belgium.
- Department of Human Genetics, KU Leuven, 3000, Leuven, Belgium.
- Medical Imaging Research Center, UZ Leuven, 3000, Leuven, Belgium.
- Department of Electrical Engineering, Processing of Speech and Images (ESAT-PSI), KU Leuven, 3000, Leuven, Belgium.
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Catholic University Leuven, 3000, Leuven, Belgium. [email protected].
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000, Leuven, Belgium. [email protected].
- Department of Dental Medicine, Karolinska Institutet, 141 04, Stockholm, Sweden. [email protected].
Abstract
Dental charting from panoramic radiographs is essential for clinical assessment, yet remains time-consuming. This study presents Panognostics-Plus, an automated cloud-based AI framework for panoramic radiograph analysis integrating dental charting and structured radiological reporting. Panognostics-Plus was developed using a retrospective dataset of 1,595 panoramic radiographs and a YOLO-based architecture comprising modules for dental anatomy, dental treatment, and periapical diagnostics. Anatomical structures and dental treatments were analyzed using a two-stage pipeline, whereas periapical lesions were analyzed by detection-and-classification. YOLOv8m, YOLOv9m, YOLO11m, and YOLO26m were benchmarked. Detection was evaluated by precision, sensitivity, F1-score, AP, and mAP; segmentation by IoU and Dice; and processing time. Model differences were assessed using Friedman tests with Holm-adjusted Wilcoxon signed-rank post-hoc comparisons. YOLO26m achieved the highest overall performance. The largest mAP0@50:0.95 gains were observed for crowns (+ 21.3%), radicular cysts (+ 20.5%), root canal treatments (+ 17.3%), bridges (+ 14.3%), periodontal bone level (+ 14.3%), and periapical granulomas (+ 13.5%). Dental implants achieved precision, sensitivity, and F1-score of 0.99, 0.98, and 0.98, respectively. Radicular cysts achieved precision, sensitivity, and F1-score of 0.95, 0.93, and 0.94 versus 0.92, 0.90, and 0.91 for periapical granulomas. Significant differences were observed in 8 of 10 detection tasks and all segmentation tasks (all p < 0.001); periodontal bone level (p = 0.193) and dental fillings (p = 0.073) were nonsignificant. Dice coefficients reached 0.97 for dental anatomy and 0.96 for dental treatments. The workflow generated a segmentation overlay, dental chart, and structured radiological report in 15 ± 1.4 s per radiograph. Panognostics-Plus may improve panoramic radiograph analysis and clinical efficiency, although further validation is required.