ArticleJournal of clinical periodontology2025
Developing Predictive Models for Periodontitis Progression Using Artificial Intelligence: A Longitudinal Cohort Study.
Article in Journal of clinical periodontology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence in Periodontology: A Systematic Review.Journal of periodontal research · 2026Pooled it
- An interdisciplinary framework for artificial intelligence, precision medicine, and ethical governance in periodontal care: a systematic review.BMC oral health · 2026Pooled it
- A Commentary on AI-based Prediction Models in Periodontitis Progression.Evidence-based dentistry · 2026Article
- In Vivo Assessment of SED: A Novel Biomechanical Indicator for Periodontal Tissue Remodeling in Clear Aligner Therapy.Annals of biomedical engineering · 2026Article
- Diagnostic and Therapeutic Approaches in Periodontology: From Traditional Concepts to Modern Innovations.Biomedicines · 2026Review
- Innovations using digital technologies for periodontal diagnosis and prognosis: a narrative review.Acta odontologica Scandinavica · 2026Review
- Unsupervised phenotyping of the periodontal architecture through high-dimensional clustering of electronic health records: A multicenter study.Journal of periodontology · 2026Article
- Prediction and risk factor analysis of dental implant failure using Cox regression and machine learning.Journal of periodontology · 2026Article
- Functional Foods and Micro- and Nanoplastics: Advances in Precision Nutritional Medicine for Oral-Gut-Brain Axis Health.Antioxidants (Basel, Switzerland) · 2026Review
- A predictive model for significant periodontal disease progression: A large-scale cohort study.Medicina oral, patologia oral y cirugia bucal · 2026Article
- Advanced Diagnostic Technologies and Molecular Biomarkers in Periodontitis: Systemic Health Implications and Translational Perspectives.Journal of clinical medicine · 2026Review
- Adjunctive Therapies in Periodontitis: Current Concepts and the Future.Journal of periodontal research · 2026Review
- Dimensionally Engineered Nanotherapeutics for Pulpitis and Periodontitis: From Structural Design to Artificial Intelligence-Enabled Precision Therapy.International journal of nanomedicine · 2026Review
- From remote screening to precision prevention: responsible multimodal AI for risk prediction and equitable oral healthcare.Frontiers in oral health · 2026Review
- Molecular cytokine-dependent mechanisms of periodontitis pathogenesis and the potential for pharmacological IL-1 modulation in disease treatment.Frontiers in dental medicine · 2026Review
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
- AI-Powered Predictive Models in Implant Dentistry: Planning, Risk Assessment, and Outcomes.Journal of clinical medicine · 2025Review
- Periodontal Medicine: The Past, the Present, the Future.Journal of periodontal research · 2025Article
- Developing Predictive Models for Periodontitis Progression Using Artificial Intelligence: A Longitudinal Cohort Study.Journal of clinical periodontology · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
aimTo construct predictive models of periodontitis progression by applying Machine Learning (ML) to baseline data from a study of periodontitis progression. MATERIALS AND
methodsLogistic regression (LR), multi-layer perceptron (MLP) and probabilistic graphic model (PGM) were utilised on data from a multi-centre longitudinal study in which periodontally healthy (n = 113) and periodontitis participants (n = 302) were examined bi-monthly for 12 months without treatment. Periodontal examination was performed, and salivary levels of 10 analytes were determined. Clinical and demographic parameters and analytes levels were input into the model. The performance of 14 models was compared using the area under the receiver operating characteristic curve (AUROC), and feature importance was assessed using SHapley Additive exPlanations (SHAP).
resultsThe PGM model (Clinical measures, saliva IL-1β, age, sex) demonstrated the best overall performance (AUROC = 0.88), compared to LR (AUROC = 0.72) and MLP (AUROC = 0.58). Although MLP had a lower Brier score (0.12), its sensitivity was 0, limiting its clinical utility. In contrast, PGM achieved a balanced sensitivity (0.55) and specificity (0.81). Feature importance analyses highlighted the number of deep periodontal pockets as a key driver of model predictions in both PGM and MLP.
conclusionsML models can predict periodontitis progression, supporting early detection strategies. Our integrative approach, combining clinical data with salivary biomarkers such as IL-1β, improved predictive accuracy.
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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.