ArticleJournal of clinical medicine2020
Transfer Learning via Deep Neural Networks for Implant Fixture System Classification Using Periapical Radiographs.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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
46 citing papers in PubMed, 116 citations in OpenAlex.
- Evaluating the Test Characteristics of a Prototype for AI-Assisted Radiographic Detection.Dentistry journal · 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
- Comparative analysis of YOLO variants for dental implant brand identification.Scientific reports · 2025Article
- Deep Spectrogram Learning for Gunshot Classification: A Comparative Study of CNN Architectures and Time-Frequency Representations.Journal of imaging · 2025Article
- Machine learning in dentistry and oral surgery: charting the course with bibliometric insights.Head & face medicine · 2025Article
- Integrating Machine Learning and Deep Learning for Predicting Non-Surgical Root Canal Treatment Outcomes Using Two-Dimensional Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Two-step deep learning models for detection and identification of the manufacturers and types of dental implants on panoramic radiographs.Odontology · 2025Article
- Optimizing dental implant identification using deep learning leveraging artificial data.Scientific reports · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System.Journal of imaging informatics in medicine · 2024Article
- Artificial intelligence for caries detection: a novel diagnostic tool using deep learning algorithms.Oral radiology · 2024Article
- Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning.Scientific reports · 2024Article
- Identification of dental implant systems from low-quality and distorted dental radiographs using AI trained on a large multi-center dataset.Scientific reports · 2024Article
- Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Deep-learning performance in identifying and classifying dental implant systems from dental imaging: a systematic review and meta-analysis.Journal of periodontal & implant science · 2024Review
- Detection of Periodontal Bone Loss on Periapical Radiographs-A Diagnostic Study Using Different Convolutional Neural Networks.Journal of clinical medicine · 2023Article
- Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study.Journal of Yeungnam medical science · 2023Article
- The preliminary in vitro study and application of deep learning algorithm in cone beam computed tomography image implant recognition.Scientific reports · 2023Article
- Deep learning and clustering approaches for dental implant size classification based on periapical radiographs.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
In the absence of accurate medical records, it is critical to correctly classify implant fixture systems using periapical radiographs to provide accurate diagnoses and treatments to patients or to respond to complications. The purpose of this study was to evaluate whether deep neural networks can identify four different types of implants on intraoral radiographs. In this study, images of 801 patients who underwent periapical radiographs between 2005 and 2019 at Yonsei University Dental Hospital were used. Images containing the following four types of implants were selected: Brånemark Mk TiUnite, Dentium Implantium, Straumann Bone Level, and Straumann Tissue Level. SqueezeNet, GoogLeNet, ResNet-18, MobileNet-v2, and ResNet-50 were tested to determine the optimal pre-trained network architecture. The accuracy, precision, recall, and F1 score were calculated for each network using a confusion matrix. All five models showed a test accuracy exceeding 90%. SqueezeNet and MobileNet-v2, which are small networks with less than four million parameters, showed an accuracy of approximately 96% and 97%, respectively. The results of this study confirmed that convolutional neural networks can classify the four implant fixtures with high accuracy even with a relatively small network and a small number of images. This may solve the inconveniences associated with unnecessary treatments and medical expenses caused by lack of knowledge about the exact type of implant.
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.