Development and clinical validation of an artificial intelligence-assisted radiographic system for predicting periodontal pocket severity using full-mouth radiographs.
Year: 2026
Type: article
Abstract: Background/purpose: Conventional periodontal probing is the gold standard for evaluating periodontal disease severity; however, it is time-consuming and susceptible to operator-dependent variability. This study developed and clinically validated an artificial intelligence (AI)-assisted radiographic system for automated periodontal disease severity assessment using full-mouth radiographs, and investigated its association with clinical periodontal probing depth. Materials and methods: A total of 655 full-mouth radiographs were retrospectively collected: 151 (3962 teeth) for AI model training and 504 (13,809 teeth) for validation. Periodontal bone loss was quantified using the periodontal bone loss (PBL) ratio and classified as mild, moderate, or severe. Clinical validation used 31 radiographs with corresponding periodontal charting records (768 teeth). Pearson correlation, simple linear regression, one-way ANOVA, and receiver operating characteristic (ROC) analyses evaluated the relationship between AI-derived values and maximum probing depth (Max PD). Results: = 0.252). ROC analysis yielded AUCs of 0.767 and 0.789 for Max PD ≥ 6 mm and ≥7 mm, respectively. Conclusion: The AI-assisted radiographic system demonstrated high diagnostic accuracy and significant correlation with clinical probing depth, supporting its role as an objective adjunctive tool for periodontal disease screening, severity stratification, and clinical decision support.
Source: PubMed
Language: English
Subfield: Oral Surgery
Field: Dentistry
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7223553134