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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.
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Saadi et al. (2026) studied this question.
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