ArticleFrontiers in artificial intelligence2022
Developing and testing a prediction model for periodontal disease using machine learning and big electronic dental record data.
Article in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 3 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
- Artificial Intelligence Models for Diagnosis of Periodontitis Using Non-Invasive Biological Markers: A Systematic Review and Meta-Analysis of Patient-Based Studies.Medical sciences (Basel, Switzerland) · 2025Pooled it
- Periodontitis Risk Assessment and Prevention Planning: Comparative Study of Multimodal Large Language Models and Periodontist Evaluations.Journal of medical Internet research · 2026Article
- Periodontitis Prediction Model Using Linked Electronic Health and Dental Records.JDR clinical and translational research · 2026Article
- Body roundness index as a novel marker of periodontitis in US adults: NHANES 2009-2014 analysis.Medicine · 2026Article
- Artificial Intelligence in Periodontology: From Automated Diagnosis to Prediction and Clinical Decision Support-A Narrative Review.Dentistry journal · 2026Review
- Multimodal Prediction of Periodontitis Using Root Exposure in Intraoral Images and Age.International dental journal · 2026Article
- Periodontal disease and cardiovascular events: mechanisms, biomarkers, and clinical translation.NPJ cardiovascular health · 2026Review
- A predictive model for significant periodontal disease progression: A large-scale cohort study.Medicina oral, patologia oral y cirugia bucal · 2026Article
- Digital Dentistry in Clinical Practice: A Scoping Review of Current Capabilities and Future Directions.International dental journal · 2026Article
- Machine learning assisted classification of periodontal health and periodontitis using alveolar bone loss measurements on bitewing radiographs.Frontiers in oral health · 2026Article
- Developing Predictive Models for Periodontitis Progression Using Artificial Intelligence: A Longitudinal Cohort Study.Journal of clinical periodontology · 2025Article
- Applications of Artificial Intelligence (AI) for Diagnosis of Periodontal/Peri-Implant Diseases: A Narrative Review.Journal of oral rehabilitation · 2025Review
- Exploring the Ethical Landscape of Artificial Intelligence in Dentistry: Insights From a Cross-Sectional Study.Cureus · 2025Article
- Application of machine learning in dentistry: insights, prospects and challenges.Acta odontologica Scandinavica · 2025Review
- Classification of periodontitis stage and grade using natural language processing techniques.PLOS digital health · 2024Article
- Data-driven prediction model for periodontal disease based on correlational feature analysis and clinical validation.Heliyon · 2024Article
- Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches.Frontiers in artificial intelligence · 2024Review
- Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite advances in periodontal disease (PD) research and periodontal treatments, 42% of the US population suffer from periodontitis. PD can be prevented if high-risk patients are identified early to provide preventive care. Prediction models can help assess risk for PD before initiation and progression; nevertheless, utilization of existing PD prediction models is seldom because of their suboptimal performance. This study aims to develop and test the PD prediction model using machine learning (ML) and electronic dental record (EDR) data that could provide large sample sizes and up-to-date information. A cohort of 27,138 dental patients and grouped PD diagnoses into: healthy control, mild PD, and severe PD was generated. The ML model (XGBoost) was trained (80% training data) and tested (20% testing data) with a total of 74 features extracted from the EDR. We used a five-fold cross-validation strategy to identify the optimal hyperparameters of the model for this one-
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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.