ArticleInternational dental journal2026
Multimodal Prediction of Periodontitis Using Root Exposure in Intraoral Images and Age.
Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION AND
aimsDespite advances in AI-based periodontitis screening, quantifiable and interpretable biomarkers from intraoral photographs remain underexplored. Therefore, this study aimed to develop a deep learning pipeline for exposed root area quantification from photographs and to evaluate its predictive value for periodontitis risk within a multimodal framework integrating age.
methodsIntraoral photographs of the mandibular anterior sextant and covariate questionnaires were obtained from 269 participants. A fine-tuned YOLOv11 segmentation model quantified tooth and exposed root surface areas, from which the exposed root ratio (ERR) was derived. ERR was combined with age and self-reported data to train four machine learning models (logistic regression, SVM, random forest, gradient boosting) for periodontitis prediction. Performance was assessed using AUROC and permutation feature importance across different feature sets.
resultsThe YOLOv11 segmentation model achieved an overall mAP@0.5 of 0.901, with mean Dice coefficients of 0.928 and 0.844 for tooth and exposed root, respectively. In the ≥35 age group, ERR-only models outperformed age-only models across all four machine learning algorithms, with statistically significant differences in 13 of 24 comparisons (mean ΔAUROC: 0.031-0.094, p < .05). Integration of ERR with age further improved predictive performance, yielding significant gains in 19 of 24 comparisons (mean ΔAUROC: 0.029-0.131, p < .05). Permutation feature importance analysis revealed ERR as the dominant predictor in the ≥45 age group, with importance scores of 0.391 and 0.366 for ERR compared to 0.151 and 0.273 for age in Gradient Boosting and Random Forest, respectively.
conclusionAI-derived ERR from mandibular anterior images is a reproducible, interpretable biomarker that outperforms age and enhances periodontitis prediction when combined with conventional risk factors. CLINICAL RELEVANCE: AI-driven quantification of ERR from intraoral photographs offers a practical, non-invasive, and cost-effective screening tool for periodontitis risk assessment in primary care and community settings, particularly among middle-aged and older populations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.