ArticleJournal of periodontology2025
Machine learning-assisted prediction of clinical responses to periodontal treatment.
Article in Journal of periodontology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Methodological Quality of Prognostic Models for Periodontitis: A Systematic Review and Critical Appraisal.Journal of clinical periodontology · 2026Pooled it
- Artificial Intelligence in Periodontology: A Systematic Review.Journal of periodontal research · 2026Pooled it
- Artificial intelligence models in cerebral infarction: performance and applications in diagnosis, classification, grading, treatment, and disease course prediction-a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Does timing of systemic antibiotics influence periodontal treatment outcomes? A randomized clinical trial.Journal of periodontology · 2026Trial
- 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
- Digital Dentistry in Clinical Practice: A Scoping Review of Current Capabilities and Future Directions.International dental journal · 2026Article
- From remote screening to precision prevention: responsible multimodal AI for risk prediction and equitable oral healthcare.Frontiers in oral health · 2026Review
- PerioTwin-DeltaCRED: a validation-aware physiological digital twin for longitudinal periodontal treatment response prediction.Frontiers in physiology · 2026Article
- Host-microbiome-immune disequilibrium in oral disease: mechanisms, dysbiosis, and precision therapeutics.Frontiers in immunology · 2026Review
- The Application and Performance of Artificial Intelligence (AI) Models in the Diagnosis, Classification, and Prediction of Periodontal Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- 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
- Interpretable machine learning modeling of treatment outcomes for silver and fluoride therapy in early childhood caries.Evidence-based dentistry · 2025Article
- Machine learning-assisted prediction of clinical responses to periodontal treatment.Journal of periodontology · 2025Article
- Discovering periodontitis biomarkers and therapeutic targets through bioinformatics and ensemble learning analysis.Scientific reports · 2025Article
- Periodontal Medicine: The Past, the Present, the Future.Journal of periodontal research · 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
- EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Periodontology: The Past, the Present, the Future.Journal of periodontal research · 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
6 authors.
Funding
Abstract
backgroundPeriodontitis is among the most prevalent chronic inflammatory conditions globally, and is associated with bone resorption, tooth loss, and systemic complications. While its treatment is largely standardized, individual outcomes vary, with some patients experiencing further disease progression despite adherence.
methodsWe developed a machine learning (ML) approach to predict individual outcomes 1 year post-treatment using retrospectively assessed baseline parameters. We trained a Random Forest model on 18 demographic, clinical, microbiological, and treatment-related features of 414 patients from randomized clinical trials (RCTs) in South America. We subsequently performed internal testing, interpretability analysis, and external testing on a second dataset of 78 patients from previous RCTs in North America and Europe exhibiting less severe disease.
resultsIn internal testing, the ML model achieved an area under the receiver operator characteristics curve (AUROC) of 0.93, an area under the precision-recall curve (AUPRC) of 0.90, an F
conclusionsOur study indicates that an ML-based approach can assist in predicting individual responses to periodontal treatment. Prospective validation is needed for clinical application. PLAIN LANGUAGE SUMMARY: Using comprehensive data from patients with periodontitis, an inflammatory condition of the tooth-supporting tissues, a machine learning model was trained to predict how well patients might respond to different treatments after 1 year. The model was externally tested in patient populations from 2 continents different from the training dataset. The results suggest that with further research and refinement, this tool could eventually become a valuable asset in personalizing treatment plans for improved patient outcomes.
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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.