ArticleFrontiers in cellular and infection microbiology2021
Machine Learning Approach Identified Multi-Platform Factors for Caries Prediction in Child-Mother Dyads.
Article in Frontiers in cellular and infection microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2022Pooled it
- Ethnicity-specific microbiome in early childhood caries: a functional perspective of oral biofilm.mSystems · 2026Article
- MicroSTNet: a spatio-temporal graph-based framework for time-series microbiome analysis.Microbial genomics · 2025Article
- A Complete Transfer Learning-Based Pipeline for Discriminating Between Select Pathogenic Yeasts from Microscopy Photographs.Pathogens (Basel, Switzerland) · 2025Article
- Early childhood caries risk prediction using machine learning approaches in Bangladesh.BMC oral health · 2025Article
- The Use of Artificial Intelligence in Caries Detection: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Trends of machine learning for dental caries research in Southeast Asia: insights from a bibliometric analysis.F1000Research · 2024Article
- Decoding children dental health risks: a machine learning approach to identifying key influencing factors.Frontiers in artificial intelligence · 2024Article
- The effect of different sweeteners on the oral microbiome: a randomized clinical exploratory pilot study.Journal of oral microbiology · 2024Article
- Early childhood caries prevention through a developmental origins model of the oral microbiome, host and oral environment, and sociodemographic influences.Quintessence international (Berlin, Germany : 1985) · 2023Article
- Influence of Artificial Intelligence-Driven Diagnostic Tools on Treatment Decision-Making in Early Childhood Caries: A Systematic Review of Accuracy and Clinical Outcomes.Dentistry journal · 2023Review
- Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)-A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Oral Fungal Infections: Past, Present, and Future.Frontiers in oral health · 2022Review
- Multimodal Data Integration Reveals Mode of Delivery and Snack Consumption Outrank Salivary Microbiome in Association With Caries Outcome in Thai Children.Frontiers in cellular and infection microbiology · 2022Article
- Assessing a Smartphone App (AICaries) That Uses Artificial Intelligence to Detect Dental Caries in Children and Provides Interactive Oral Health Education: Protocol for a Design and Usability Testing Study.JMIR research protocols · 2021Article
- Artificial intelligence based techniques for caries risk prediction and assessment: A scoping review.Journal of oral biology and craniofacial researchArticle
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Untreated tooth decays affect nearly one third of the world and is the most prevalent disease burden among children. The disease progression of tooth decay is multifactorial and involves a prolonged decrease in pH, resulting in the demineralization of tooth surfaces. Bacterial species that are capable of fermenting carbohydrates contribute to the demineralization process by the production of organic acids. The combined use of machine learning and 16s rRNA sequencing offers the potential to predict tooth decay by identifying the bacterial community that is present in an individual's oral cavity. A few recent studies have demonstrated machine learning predictive modeling using 16s rRNA sequencing of oral samples, but they lack consideration of the multifactorial nature of tooth decay, as well as the role of fungal species within their models. Here, the oral microbiome of mother-child dyads (both healthy and caries-active) was used in combination with demographic-environmental factors and relevant fungal information to create a multifactorial machine learning model based on the LASSO-penalized logistic regression. For the children, not only were several bacterial species found to be caries-associated (
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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.