ArticleJournal of clinical periodontology2022
Systematic comparison of machine learning algorithms to develop and validate predictive models for periodontitis.
Article in Journal of clinical periodontology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
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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
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Artificial Intelligence in Periodontology: A Systematic Review.Journal of periodontal research · 2026Pooled it
- Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities.BMC oral health · 2025Pooled it
- Periodontitis Prediction Model Using Linked Electronic Health and Dental Records.JDR clinical and translational research · 2026Article
- Bridging the Implementation Gap: Artificial Intelligence in Periodontology from Proof-of-Concept to Clinical Governance-A Systematic Scoping Review with Evidence from Kazakhstan.Dentistry journal · 2026Review
- Diagnostic and Therapeutic Approaches in Periodontology: From Traditional Concepts to Modern Innovations.Biomedicines · 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
- Investigating the mechanisms of malignant progression in colorectal cancer using weighted gene co-expression network analysis and machine learning.Journal of molecular histology · 2026Article
- Non-invasive periodontal screening using self-reported-oral-health (SROH) questionnaire and salivary biomarkers: development and validation of machine learning models.BMC oral health · 2026Article
- Artificial intelligence in periodontal disease research: a bibliometric and visualized analysis of global research trends (2007-2025).The Saudi dental journal · 2026Review
- The role of triglyceride-glucose (TyG) index in predicting mortality risk in critically ill patients with sepsis: a machine learning model in a cohort study.BMC medical informatics and decision making · 2026Article
- An explainable and transparent machine learning approach for predicting dental caries: a cross-national validation study.BMC oral health · 2026Article
- Harnessing generative artificial intelligence for periodontitis prediction: a machine learning approach integrating systemic health indicators for precision oral health in resource-limited settings.Frontiers in dental medicine · 2026Article
- Predicting plaque-gingivitis risk in schoolchildren using an interpretable machine learning model: a cross-sectional study.BMC oral health · 2025Article
- Diagnostic Support in Dentistry Through Artificial Intelligence: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- Developing Predictive Models for Periodontitis Progression Using Artificial Intelligence: A Longitudinal Cohort Study.Journal of clinical periodontology · 2025Article
- [Applications and perspectives of artificial intelligence in periodontology].Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2025Review
- Comparison of Supervised Machine Learning Models to Logistic Regression Model Using Tooth-Related Factors to Predict the Outcome of Nonsurgical Periodontal Treatment.Diagnostics (Basel, Switzerland) · 2025Article
- Determination of the Stage of Periodontitis with 20 ng/mL Cut-Off aMMP-8 Mouth Rinse Test and Polynomial Functions in a Mobile Application.Diagnostics (Basel, Switzerland) · 2025Article
- Periodontitis Home Screening with Mouth Rinse Cut-Off 20 Ng/mL aMMP-8 Test and Mobile Application.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThe aim of this study was to compare the validity of different machine learning algorithms to develop and validate predictive models for periodontitis. MATERIALS AND
methodsUsing national survey data from Taiwan (n = 3453) and the United States (n = 3685), predictors of periodontitis were extracted from the datasets and pre-processed, and then 10 machine learning algorithms were trained to develop predictive models. The models were validated both internally (bootstrap sampling) and externally (alternative country's dataset). The algorithms were compared across six performance metrics ([i] area under the curve for the receiver operating characteristic [AUC], [ii] accuracy, [iii] sensitivity, [iv] specificity, [v] positive predictive value, and [vi] negative predictive value) and two methods of data pre-processing ([i] machine-learning-based feature selection and [ii] dimensionality reduction into principal components).
resultsMany algorithms showed extremely strong performance during internal validation (AUC > 0.95, accuracy > 95%). However, this was not replicated in external validation, where predictive performance of all algorithms dropped off drastically. Furthermore, predictive performance differed according to data pre-processing methodology and the cohort on which they were trained.
conclusionsLarger sample sizes and more complex predictors of periodontitis are required before machine learning can be leveraged to its full potential.
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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.