ArticleJournal of dental sciences2024
Establishing a novel deep learning model for detecting peri-implantiti
Article in Journal of dental sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- 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
- Deep Learning Based on Swin-Transformer and 3D U-Net: Implant Three-Dimensional Position Planning.International dental journal · 2026Article
- Artificial Intelligence for Radiographic Diagnosis of Peri-Implantitis: A Comprehensive Review on Detection, Measurement, and Risk Stratification.Journal of clinical medicine · 2026Review
- AI detection of peri-implantitis on 2D radiographs: a systematic review and diagnostic accuracy assessment.Odontology · 2026Review
- Radiomics analysis of panoramic radiographs using machine learning for the detection of peri-implantitis.BMC oral health · 2026Article
- Detection and Classification of Peri-Implant Marginal Bone Loss in Cone-Beam Computed Tomography Using a Deep Learning Approach.Clinical and experimental dental research · 2026Article
- A web-based deep learning cascade for automated detection and quantification of marginal bone loss.BMC oral health · 2026Article
- Application of Artificial Intelligence in Education of Prosthodontics and Implant Dentistry: A Review.Advances in medical education and practice · 2026Review
- Automated assessment of peri-implant disease severity by deep learning and image processing in periapical radiographs.Journal of dental sciences · 2026Article
- A visualized AI-assisted framework for pre-surgical evaluation of implant pathways on periapical radiographs.Journal of dental sciences · 2026Article
- Precision immunopharmacology in peri-implantitis management: from molecular mechanisms to advanced therapeutic strategies.Frontiers in immunology · 2026Review
- AI-Powered Predictive Models in Implant Dentistry: Planning, Risk Assessment, and Outcomes.Journal of clinical medicine · 2025Review
- Performance of artificial intelligence-based diagnosis and classification of peri-implantitis compared with periodontal surgeon assessment: a pilot study of panoramic radiograph analysis.Journal of periodontal & implant science · 2025Article
- Accuracy of Artificial Intelligence Models in Detecting Peri-Implant Bone Loss: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Predicting interactome networks of up/down regulated proteins and drug-gene interaction analysis associated with peri-implantitis.Dental research journal · 2025Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background/purposeThe diagnosis of peri-implantitis using periapical radiographs is crucial. Recently, artificial intelligence may apply in radiographic image analysis effectively. The aim of this study was to differentiate the degree of marginal bone loss of an implant, and also to classify the severity of peri-implantitis using a deep learning model. MATERIALS AND
methodsA dataset of 800 periapical radiographic images were divided into training (
resultsConsidering the classification performance, the specificity was 100%, precision was 100%, recall was 94.44%, and F1 score was 97.10%.
conclusionResults of this study suggested that implants can be identified from periapical radiographic images using deep learning-based object detection. This identification system could help dentists and patients suffering from implant problems. However, more images of other implant systems are needed to increase the learning performance to apply this system in clinical practice.
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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.