Agreement between artificial intelligence and periodontists in the detection of periodontal bone loss on panoramic radiographs
Clinical Oral Investigations, vol.30, no.10, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 30 Issue: 10
- Publication Date: 2026
- Doi Number: 10.1007/s00784-026-07128-8
- Journal Name: Clinical Oral Investigations
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, MEDLINE, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest), Pharma Collection (ProQuest)
- Keywords: Alveolar bone measurement, Automated landmark detection, Deep learning, Dental image analysis, Image segmentation, Point-cloud analysis
- Anadolu University Affiliated: No
Abstract
Objectives: To evaluate the agreement between radiographic bone level measurements generated by a deep learning model and those derived from expert periodontist annotations on panoramic radiographs. Materials and methods: In this retrospective single-center radiographic study, periodontal bone loss was segmented by a YOLOv8x-based deep learning model and independently manually segmented by four periodontists. The artificial intelligence (AI)- and periodontist-generated segmentations were converted into point clouds for radiographic bone level measurements, and the overall agreement and that for six tooth regions was assessed using Bland–Altman analysis, with additional error and association metrics calculated. Results: Across 2,037 teeth, the mean absolute error (MAE) between the AI-derived and periodontist consensus measurements was 5.08% points of root length. The measurements revealed a strong association (Pearson’s r = 0.80, Spearman’s ρ = 0.80). Agreement was highest in the upper anterior region (MAE = 4.42; R² = 0.75; r = 0.90) and lowest in the lower molar region (MAE = 7.66; R² = 0.26; r = 0.65). Bland–Altman analysis revealed a mean bias of 1.61% points, with wide limits of agreement across all regions. Conclusion: AI-derived measurements were strongly associated periodontist consensus measurements, with only a small systematic difference. However, the limits of agreement, particularly in the molar regions, were too wide to support the use of AI-derived measurements for individual tooth-level assessment without clinician oversight. Clinical relevance: The evaluated deep learning system may facilitate radiographic bone level assessment on panoramic radiographs but currently requires clinician oversight, especially in the molar regions.