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Multimodal Prediction of Periodontitis Using Root Exposure in Intraoral Images and Age.

Sohee Kang¹, Hyeonjeong Go², Young-Eun Kwon³

¹Department of Dentistry, College of Medicine, Yeungnam University, Daegu, Republic of Korea.

International Dental Journal

|May 12, 2026

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This study developed an AI tool to measure exposed root ratio (ERR) from dental photos, showing it predicts periodontitis risk better than age alone, especially in older adults.

Area of Science:

Artificial Intelligence in Dentistry
Biomarker Discovery
Periodontitis Research

Background:

AI-based periodontitis screening lacks quantifiable biomarkers from intraoral images.
Exposed root area quantification from photographs is underexplored.

Purpose of the Study:

Develop a deep learning pipeline to quantify exposed root area (ERR) from intraoral photographs.
Evaluate the predictive value of ERR for periodontitis risk, integrating it with age.

Main Methods:

A YOLOv11 segmentation model was used to quantify tooth and exposed root areas from 269 participants' intraoral photographs.
The exposed root ratio (ERR) was calculated and combined with age for periodontitis risk prediction using machine learning models.
Performance was assessed using AUROC and permutation feature importance.

Keywords:

Artificial intelligence Gingival recession Intraoral photography Periodontitis Tooth root

Related Experiment Videos

Last Updated: May 14, 2026

Induction of Periodontitis via a Combination of Ligature and Lipopolysaccharide Injection in a Rat Model

Induction of Periodontitis via a Combination of Ligature and Lipopolysaccharide Injection in a Rat Model

Published on: February 17, 2023

Main Results:

The AI model achieved high accuracy in segmenting teeth (Dice 0.928) and exposed roots (Dice 0.844).
ERR-only models outperformed age-only models for periodontitis prediction in individuals aged 35 and older.
Integrating ERR with age significantly improved predictive performance, with ERR being a dominant predictor in older age groups.

Conclusions:

AI-derived ERR is a reproducible and interpretable biomarker for periodontitis risk assessment.
This AI-driven approach offers a practical, non-invasive tool for periodontitis screening in various healthcare settings, particularly for middle-aged and older populations.