ArticlePloS one2021
Predictors of tooth loss: A machine learning approach.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Candidate loci shared among periodontal disease, diabetes and bone density.Frontiers in endocrinology · 2022Pooled it
- Identifying High-Priority Profiles for Edentulism and Other Health Outcomes Through Population-Based Clustering.AJPM focus · 2026Article
- Algorithmovigilance in AI-Based Oral-Health Surveillance: Temporal Drift, Cross-Survey Differences, and Predictor-Level Structural Stability in Population-Level Severe Tooth Loss Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- [Formula: see text] : explainable attentive transformers for identifying the factors influencing dental visits to enhance dental data completeness.BMC oral health · 2025Article
- Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records.Scientific reports · 2025Article
- Artificial Intelligence-Related Dental Research: Bibliometric and Altmetric Analysis.International dental journal · 2025Article
- Article
- Employing AI tools to predict features for dental care use in the United States during the global respiratory illness outbreak.Frontiers in public health · 2025Article
- Tooth loss in breast cancer patients: A comparison between tamoxifen-treated and non-treated patients.Medicina oral, patologia oral y cirugia bucal · 2024Observational
- Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults.BMC oral health · 2024Article
- Machine learning to predict untreated dental caries in adolescents.BMC oral health · 2024Article
- Patterns and predictors of tooth loss among partially dentate individuals in Jordan: A cross-sectional study.The Saudi dental journal · 2024Article
- Last dental visit and severity of tooth loss: a machine learning approach.BMC research notes · 2023Article
- Second Opinion for Non-Surgical Root Canal Treatment Prognosis Using Machine Learning Models.Diagnostics (Basel, Switzerland) · 2023Article
- Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment.Journal of personalized medicine · 2022Review
- Diagnosis of Tooth Prognosis Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2022Article
- Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care.PloS one · 2022Article
- The effect of denture-wearing on physical activity is associated with cognitive impairment in the elderly: A cross-sectional study based on the CHARLS database.Frontiers in neuroscience · 2022Article
- Number of teeth is associated with all-cause and disease-specific mortality.BMC oral health · 2021Article
- Evaluation of the predictors of tooth loss using artificial intelligence-based machine learning approach: A retrospective study.Journal of Indian Society of PeriodontologyArticle
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
introductionLittle is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual's quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models.
methodsWe used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values.
resultsThe extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone.
conclusionsFuture application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.
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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.