ReviewJournal of oral rehabilitation2025
Applications of Artificial Intelligence (AI) for Diagnosis of Periodontal/Peri-Implant Diseases: A Narrative Review.
Review in Journal of oral rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Bridging the Implementation Gap: Artificial Intelligence in Periodontology from Proof-of-Concept to Clinical Governance-A Systematic Scoping Review with Evidence from Kazakhstan.Dentistry journal · 2026Review
- From Panoramic Radiographs to AI-Assisted Radiographic Periodontal Charting: Current Evidence and Future Perspectives.Dentistry journal · 2026Review
- Dual-Biomarker Oral-Rinse Testing for aMMP-8 and Calprotectin: A Potential Adjunctive Tool for Risk Stratification and Interdisciplinary Referral in Periodontitis and Type 2 Diabetes.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence for Radiographic Diagnosis of Peri-Implantitis: A Comprehensive Review on Detection, Measurement, and Risk Stratification.Journal of clinical medicine · 2026Review
- Advanced Diagnostic Technologies and Molecular Biomarkers in Periodontitis: Systemic Health Implications and Translational Perspectives.Journal of clinical medicine · 2026Review
- Review
- Segmentation-Based Multi-Class Detection and Radiographic Charting of Periodontal and Restorative Conditions on Bitewing Radiographs Using Deep Learning.Diagnostics (Basel, Switzerland) · 2026Article
- PerioTwin-DeltaCRED: a validation-aware physiological digital twin for longitudinal periodontal treatment response prediction.Frontiers in physiology · 2026Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) and various subunits of AI such as artificial neural networks (ANN), Convolutional neural networks (CNNs), machine learning (ML), deep learning (DL) and deep neural networks (DNN) are being tried to diagnose and plan treatment for periodontal diseases.
aimThis narrative review aims to discuss the current evidence on the applications of AI for the diagnosis and risk prediction of periodontal/peri-implant diseases.
methodA search strategy with the following keywords: (Artificial intelligence [MeSH Terms]) AND (Periodontal disease [MeSH Terms]) was used to search for articles from 2000 to 2024.
resultsAI models using patient-related data, signs and symptoms of the disease, immunological biomarkers and microbial profiles aid in effective diagnosis and planning treatment. AI is also used in periodontal diagnosis of pathological and anatomical landmarks such as cementoenamel junction, bone levels, furcation defects, nature and system of dental implants placed, degree of implant or tooth fractures and periapical pathology, assessing the severity and grading of periodontal or peri-implant disease/conditions, assessing the signs and symptoms of periodontal/peri-implant disease and determining the prognosis of implant and periodontal treatment. Studies have compared the diagnosis made by dentists and AI-based models and found AI models to be more effective and quicker in diagnosis than dentists. DISCUSSION: AI-based tools such as DL, ML, CNN, and ANN are more effective and quicker for timely diagnosis, risk assessment, and treatment plans for periodontal and peri-implant disease diagnosis. DL and CNN are the most commonly used tools for the diagnosis of bone levels around teeth or implants, periodontal disease staging and severity, and location of anatomical structures and teeth.
conclusionAI and its subsets are promising tools for the diagnosis/risk prediction and treatment planning for periodontal and peri-implant diseases.
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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.