ArticleScientific reports2021
Predictive modeling for peri-implantitis by using machine learning techniques.
Article in Scientific reports, 2021. 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.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic Accuracy of Deep Learning Models in Detecting Peri-Implant Marginal Bone Loss: A Systematic Review and Meta-Analysis.Clinical oral implants research · 2026Pooled it
- Prediction models for the complication incidence and survival rate of dental implants-a systematic review and critical appraisal.International journal of implant dentistry · 2025Pooled it
- Role of hyaluronic acid in the treatment of peri-implant diseases: results of a meta-analysis.Frontiers in oral health · 2025Pooled it
- Prediction and risk factor analysis of dental implant failure using Cox regression and machine learning.Journal of periodontology · 2026Article
- Artificial Intelligence in Implant Dentistry: Clinical Validity, Diagnostic Performance, Surgical Planning, and Medico-Legal Implications-A Narrative Review.Dentistry journal · 2026Review
- Radiomics analysis of panoramic radiographs using machine learning for the detection of peri-implantitis.BMC oral health · 2026Article
- Enhancing dental implant outcomes in type 2 diabetes: Addressing inflammation and risk factors.World journal of diabetes · 2026Article
- Oral-fluid inflammatory and oxidative-stress biomarkers for peri-implantitis in Asian adults: a systematic review and stratified random-effects meta-analysis.Open life sciences · 2026Article
- Automated assessment of peri-implant disease severity by deep learning and image processing in periapical radiographs.Journal of dental sciences · 2026Article
- Precision immunopharmacology in peri-implantitis management: from molecular mechanisms to advanced therapeutic strategies.Frontiers in immunology · 2026Review
- Applications of Artificial Intelligence (AI) for Diagnosis of Periodontal/Peri-Implant Diseases: A Narrative Review.Journal of oral rehabilitation · 2025Review
- Accuracy of Artificial Intelligence Models in Detecting Peri-Implant Bone Loss: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Exploring AI-Driven Machine Learning Approaches for Optimal Classification of Peri-Implantitis Based on Oral Microbiome Data: A Feasibility Study.Diagnostics (Basel, Switzerland) · 2025Article
- Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review.Annals of biomedical engineering · 2024Review
- Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review.Bioengineering (Basel, Switzerland) · 2024Review
- Establishing a novel deep learning model for detecting peri-implantitiJournal of dental sciences · 2024Article
- Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches.Frontiers in artificial intelligence · 2024Review
- Deep learning-based prediction of osseointegration for dental implant using plain radiography.BMC oral health · 2023Article
- Early diagnosis for the onset of peri-implantitis based on artificial neural network.Open life sciences · 2023Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.
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