ArticleJournal of clinical medicine2021
Peri-Implant Bone Loss Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical Radiographs.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline 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
- Comparative assessment of CBCT and digital radiography in quantifying periapical pathology (in vitro study).BMC oral health · 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
- Accuracy of Deep Learning Models in Detecting Mandibular Furcation Defects on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- 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
- Deep Learning-Based Classification of Zirconia and Metal-Supported Porcelain Fixed Restorations on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- A web-based deep learning cascade for automated detection and quantification of marginal bone loss.BMC oral health · 2026Article
- Performance Validation of ORTHOSEG, a Novel Artificial Intelligence Tool for the Segmentation of Orthopantomographs and Intra-Oral X-Rays.Clinics and practice · 2026Article
- In Silico possibilities to understand peri-implant bone healing- state of the Art.International journal of implant dentistry · 2025Review
- Development and validation of a keypoint region-based convolutional neural network to automate thoracic Cobb angle measurements using whole-spine standing radiographs.Acta neurochirurgica · 2025Article
- Applications of Artificial Intelligence (AI) for Diagnosis of Periodontal/Peri-Implant Diseases: A Narrative Review.Journal of oral rehabilitation · 2025Review
- Diagnostic Efficiency of an Artificial Intelligence-Based Technology in Dental Radiography.Bulletin of experimental biology and medicine · 2025Article
- Accuracy of Artificial Intelligence Models in Detecting Peri-Implant Bone Loss: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Tooth numbering with polygonal segmentation on periapical radiographs: an artificial intelligence study.Clinical oral investigations · 2024Article
- Establishing a novel deep learning model for detecting peri-implantitiJournal of dental sciences · 2024Article
- "Determining the efficacy of a machine learning model for measuring periodontal bone loss".BMC oral health · 2024Article
- Deep learning and clustering approaches for dental implant size classification based on periapical radiographs.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Determining the peri-implant marginal bone level on radiographs is challenging because the boundaries of the bones around implants are often unclear or the heights of the buccal and lingual bone levels are different. Therefore, a deep convolutional neural network (CNN) was evaluated for detecting the marginal bone level, top, and apex of implants on dental periapical radiographs. An automated assistant system was proposed for calculating the bone loss percentage and classifying the bone resorption severity. A modified region-based CNN (R-CNN) was trained using transfer learning based on Microsoft Common Objects in Context dataset. Overall, 708 periapical radiographic images were divided into training (
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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.