Development and clinical validation of an artificial intelligence-assisted radiographic system for predicting periodontal pocket severity using full-mouth radiographs.
- 1Orthodontic Department, Chung Shan Medical University Hospital, Taichung, Taiwan.
- 2School of Dentistry, College of Oral Medicine, Chung Shan Medical University, Taichung, Taiwan.
- 3Dental Department, Chung Shan Medical University Hospital, Taichung, Taiwan.
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
The AI model achieved an overall classification accuracy of 90.48% (range: 90.5% -96.8% across severity categories). Misclassifications occurred only between adjacent severity levels. The severe category demonstrated the highest precision (96.5%) and specificity (99.0%); the moderate category achieved the highest sensitivity (98.5%). AI-derived values were moderately correlated with Max PD (r = 0.502, P < 0.001; R² = 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.
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BibTeX
@article{ho2026development,
title = {{Development and clinical validation of an artificial intelligence-assisted radiographic system for predicting periodontal pocket severity using full-mouth radiographs.}},
author = {Yi-Tai Ho and Tsui-Hsien Huang and Cheng-Han Shie and Tao-Wei Chang and Chun-Te Ho and Chia-Tze Kao},
journal = {Journal of dental sciences},
year = {2026},
month = oct,
doi = {10.1016/j.jds.2025.07.3589},
url = {https://europepmc.org/article/MED/42923715},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Development and clinical validation of an artificial intelligence-assisted radiographic system for predicting periodontal pocket severity using full-mouth radiographs.
AU - Yi-Tai Ho
AU - Tsui-Hsien Huang
AU - Cheng-Han Shie
AU - Tao-Wei Chang
AU - Chun-Te Ho
AU - Chia-Tze Kao
PY - 2026
DA - 2026/10/01
JO - Journal of dental sciences
DO - 10.1016/j.jds.2025.07.3589
UR - https://europepmc.org/article/MED/42923715
AB - 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 The AI model achieved an overall classification accuracy of 90.48% (range: 90.5% -96.8% across severity categories). Misclassifications occurred only between adjacent severity levels. The severe category demonstrated the highest precision (96.5%) and specificity (99.0%); the moderate category achieved the highest sensitivity (98.5%). AI-derived values were moderately correlated with Max PD (r = 0.502, P < 0.001; R² = 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.
SN - 1991-7902
SN - 2213-8862
ER -