Journal of dental sciences
Development and clinical validation of an artificial intelligence-assisted radiographic system for predicting periodontal pocket severity using full-mouth radiographs
By Ho YT, Huang TH, Shie CH and 3 others.
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).