ArticleInternational dental journal2018
Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles.
Article in International dental journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05348109 (Assessment of Decision Support System Software in Extraction and Anchorage Planning Among Adult Patients Using Computer Algorithm), which is not on this map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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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.
Assessment of Decision Support System Software in Extraction and Anchorage Planning Among Adult Patients Using Computer Algorithm
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Microbial Markers for Diagnosis and Risk Assessment for Periodontal Diseases: A Systematic Literature Search and Narrative Synthesis.Journal of clinical periodontology · 2025Pooled it
- 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
- Machine learning-assisted prediction of clinical responses to periodontal treatment.Journal of periodontology · 2025Article
- Applications of Artificial Intelligence (AI) for Diagnosis of Periodontal/Peri-Implant Diseases: A Narrative Review.Journal of oral rehabilitation · 2025Review
- Evaluating the Accuracy, Reliability, Consistency, and Readability of Different Large Language Models in Restorative Dentistry.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2025Article
- Review
- Machine learning models for prognosis prediction in regenerative endodontic procedures.BMC oral health · 2025Article
- Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study.International dental journal · 2025Article
- Establishment and evaluation of a deep learning-based tooth wear severity grading system using intraoral photographs.Journal of dental sciences · 2025Article
- Data-driven prediction model for periodontal disease based on correlational feature analysis and clinical validation.Heliyon · 2024Article
- Recent advances in nanomaterial-based biosensor for periodontitis detection.Journal of biological engineering · 2024Review
- Article
- A brief exploration of artificial intelligence in dental healthcare: a narrative review.F1000Research · 2024Review
- Role of Biofilms in Waste Water Treatment.Applied biochemistry and biotechnology · 2023Review
- Artificial Intelligence in Periodontology: A Scoping Review.Dentistry journal · 2023Article
- Responses of Salmonella biofilms to oxidizing biocides: Evidence of spatial clustering.Environmental microbiology · 2022Article
- Machine Learning Data Analysis Highlights the Role ofBiomedicines · 2022Article
- Article
- Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis.Frontiers in genetics · 2022Article
- Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review.Journal of clinical and translational research · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe existence of specific microbial profiles for different periodontal conditions is still a matter of debate. The aim of this study was to test the hypothesis that 40 bacterial species could be used to classify patients, utilising machine learning, into generalised chronic periodontitis (ChP), generalised aggressive periodontitis (AgP) and periodontal health (PH).
methodSubgingival biofilm samples were collected from patients with AgP, ChP and PH and analysed for their content of 40 bacterial species using checkerboard DNA-DNA hybridisation. Two stages of machine learning were then performed. First of all, we tested whether there was a difference between the composition of bacterial communities in PH and in disease, and then we tested whether a difference existed in the composition of bacterial communities between ChP and AgP. The data were split in each analysis to 70% train and 30% test. A support vector machine (SVM) classifier was used with a linear kernel and a Box constraint of 1. The analysis was divided into two parts.
resultsOverall, 435 patients (3,915 samples) were included in the analysis (PH = 53; ChP = 308; AgP = 74). The variance of the healthy samples in all principal component analysis (PCA) directions was smaller than that of the periodontally diseased samples, suggesting that PH is characterised by a uniform bacterial composition and that the bacterial composition of periodontally diseased samples is much more diverse. The relative bacterial load could distinguish between AgP and ChP.
conclusionAn SVC classifier using a panel of 40 bacterial species was able to distinguish between PH, AgP in young individuals and ChP.
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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.