ArticleJournal of clinical periodontology2024
Suitability of machine learning models for prediction of clinically defined Stage III/IV periodontitis from questionnaires and demographic data in Danish cohorts.
Article in Journal of clinical periodontology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 4 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 4 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- 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
- Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities.BMC oral health · 2025Pooled it
- Diagnostic Accuracy of Self-Reported Questionnaires for Detecting Periodontitis Across Multiple Cultures and Geographic Locations: A Systematic Review and Meta-Analysis.Journal of clinical periodontology · 2025Pooled it
- Multimodal Prediction of Periodontitis Using Root Exposure in Intraoral Images and Age.International dental journal · 2026Article
- Prediction and risk factor analysis of dental implant failure using Cox regression and machine learning.Journal of periodontology · 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
- LncRNA FAM30A as a potential biomarker associated with periodontitis and its role in inflammatory responses and osteogenesis.BMC oral health · 2026Article
- An explainable and transparent machine learning approach for predicting dental caries: a cross-national validation study.BMC oral health · 2026Article
- A Crosstalk Between Periodontal Disease and Human Immunodeficiency Virus: Application of Artificial Intelligence and Machine Learning in Risk Assessment and Diagnosis-A Narrative Review.Dentistry journal · 2025Review
- Prediction of Periodontal Disease Progression During Supportive Periodontal Therapy Using an Oral Fluid Lactate Dehydrogenase Activity-Based Test Strip.Journal of clinical medicine · 2025Article
- Developing Predictive Models for Periodontitis Progression Using Artificial Intelligence: A Longitudinal Cohort Study.Journal of clinical periodontology · 2025Article
- Evaluation of circulating IgG antibodies against Porphyromonas gingivalis or its gingipains as serological markers of periodontitis and carriage of the bacterium.Journal of periodontology · 2025Article
- Suitability of machine learning models for prediction of clinically defined Stage III/IV periodontitis from questionnaires and demographic data in Danish cohorts.Journal of clinical periodontology · 2024Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
aimTo evaluate if, and to what extent, machine learning models can capture clinically defined Stage III/IV periodontitis from self-report questionnaires and demographic data. MATERIALS AND
methodsSelf-reported measures of periodontitis, demographic data and clinically established Stage III/IV periodontitis status were extracted from two Danish population-based cohorts (The Copenhagen Aging and Midlife Biobank [CAMB] and The Danish Health Examination Survey [DANHES]) and used to develop cross-validated machine learning models for the prediction of clinically established Stage III/IV periodontitis. Models were trained using 10-fold cross-validations repeated three times on the CAMB dataset (n = 1476), and the resulting models were validated in the DANHES dataset (n = 3585).
resultsThe prevalence of Stage III/IV periodontitis was 23.2% (n = 342) in the CAMB dataset and 9.3% (n = 335) in the DANHES dataset. For the prediction of clinically established Stage III/IV periodontitis in the CAMB cohort, models reached area under the receiver operating characteristics (AUROCs) of 0.67-0.69, sensitivities of 0.58-0.64 and specificities of 0.71-0.80. In the DANHES cohort, models derived from the CAMB cohort achieved AUROCs of 0.64-0.70, sensitivities of 0.44-0.63 and specificities of 0.75-0.84.
conclusionsApplying cross-validated machine learning algorithms to demographic data and self-reported measures of periodontitis resulted in models with modest capabilities for the prediction of Stage III/IV periodontitis in two Danish cohorts.
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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.