ArticleBiomolecules2020
Deep Neural Networks for Dental Implant System Classification.
Article in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis 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
70 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Deep Learning-Based Identification of Dental Implant Systems from Two-Dimensional Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Fully automated deep learning for the identification and classification of dental implant brands from multimodal radiographic images.Clinical oral investigations · 2026Article
- Classification of dental implants using supervised deep learning from dental radiographs: A scoping review.Imaging science in dentistry · 2026Review
- Deep Learning-Based Classification of Zirconia and Metal-Supported Porcelain Fixed Restorations on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Implant Surface Characteristics and Peri-Implant Outcomes: A Systematic Review of Clinical and Microbiological Evidence.Bioengineering (Basel, Switzerland) · 2026Review
- Evaluating the Test Characteristics of a Prototype for AI-Assisted Radiographic Detection.Dentistry journal · 2026Article
- Advanced deep learning techniques for classifying dental conditions using panoramic X-ray images.BMC oral health · 2026Article
- Evaluation of the diagnostic reliability of an AI prototype in detecting clinical features from dental photographs-original research.Frontiers in dental medicine · 2026Article
- Development and evaluation of an AI model for dental implant type detection: A comparison of diagnostic accuracy between a deep learning model and dental professionals.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2026Article
- AI-assisted radiographic identification of original vs. replica dental implants: comparing accuracy of human experts vs. probabilistic and deterministic AI.International journal of implant dentistry · 2025Article
- Efficacy of deep learning models and dental professionals in identifying dental implants.Imaging science in dentistry · 2025Article
- Mathematical and AI-Based Predictive Modelling for Dental Caries Risk Using Clinical and Behavioural Parameters.Bioengineering (Basel, Switzerland) · 2025Article
- Applications of Artificial Intelligence (AI) for Diagnosis of Periodontal/Peri-Implant Diseases: A Narrative Review.Journal of oral rehabilitation · 2025Review
- Evaluation of Caries Detection on Bitewing Radiographs: A Comparative Analysis of the Improved Deep Learning Model and Dentist Performance.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2025Article
- Optimized classification of dental implants using convolutional neural networks and pre-trained models with preprocessed data.BMC oral health · 2025Article
- Two-step deep learning models for detection and identification of the manufacturers and types of dental implants on panoramic radiographs.Odontology · 2025Article
- Deep learning algorithms for detecting fractured instruments in root canals.BMC oral health · 2025Article
- Optimizing dental implant identification using deep learning leveraging artificial data.Scientific reports · 2025Article
- Artificial intelligence in dentistry: Assessing the informational quality of YouTube videos.PloS one · 2025Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.
Indexed as
Identifiers
What OpenQuestion holds
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.