ArticleImaging science in dentistry2022
Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study.
Article in Imaging science in dentistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Classification of dental implants using supervised deep learning from dental radiographs: A scoping review.Imaging science in dentistry · 2026Review
- How artificial intelligence is reshaping citation impact in oral and maxillofacial radiology journals: an 8-year analysis with editorial and clinical implications (JCR 2017-2024).Frontiers in research metrics and analytics · 2026Article
- Automated assessment of peri-implant disease severity by deep learning and image processing in periapical radiographs.Journal of dental sciences · 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
- Can artificial intelligence optimize treatment planning and outcome prediction in fixed tooth- and implant-supported prosthodontics? A scoping review.BMC oral health · 2025Article
- Applications of Artificial Intelligence (AI) for Diagnosis of Periodontal/Peri-Implant Diseases: A Narrative Review.Journal of oral rehabilitation · 2025Review
- Can a large language model create acceptable dental board-style examination questions? A cross-sectional prospective study.Journal of dental sciences · 2025Article
- Race to the Moon or the Bottom? Applications, Performance, and Ethical Considerations of Artificial Intelligence in Prosthodontics and Implant Dentistry.Dentistry journal · 2024Review
- Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs.Bioengineering (Basel, Switzerland) · 2024Article
- Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review.Annals of biomedical engineering · 2024Review
- Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Can deep learning identify humans by automatically constructing a database with dental panoramic radiographs?PloS one · 2024Article
- Deep learning and clustering approaches for dental implant size classification based on periapical radiographs.Scientific reports · 2023Article
- A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.Diagnostics (Basel, Switzerland) · 2023Review
- Automatic diagnosis of retention pseudocyst in the maxillary sinus on panoramic radiographs using a convolutional neural network algorithm.Scientific reports · 2023Article
- Automated detection of dental restorations using deep learning on panoramic radiographs.Dento maxillo facial radiology · 2022Article
- Deep learning-based dental implant recognition using synthetic X-ray images.Medical & biological engineering & computing · 2022Article
- Automatic feature segmentation in dental panoramic radiographs.Science progressArticle
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6 authors.
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Abstract
Purpose: This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods: Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III (Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant (Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy. Results: When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy. Conclusion: Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.
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