ArticleJournal of Yeungnam medical science2023
Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study.
Article in Journal of Yeungnam medical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- A web-based deep learning cascade for automated detection and quantification of marginal bone loss.BMC oral health · 2026Article
- Automated assessment of peri-implant disease severity by deep learning and image processing in periapical radiographs.Journal of dental sciences · 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
- Comparative analysis of YOLO variants for dental implant brand identification.Scientific reports · 2025Article
- Assessment of the Diagnostic Accuracy of Artificial Intelligence Software in Identifying Common Periodontal and Restorative Dental Conditions (Marginal Bone Loss, Periapical Lesion, Crown, Restoration, Dental Caries) in Intraoral Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Optimized classification of dental implants using convolutional neural networks and pre-trained models with preprocessed data.BMC oral health · 2025Article
- Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Emerging technologies in the field of medicine presented at the Consumer Electronics Show 2025.Journal of Yeungnam medical science · 2025Article
- Race to the Moon or the Bottom? Applications, Performance, and Ethical Considerations of Artificial Intelligence in Prosthodontics and Implant Dentistry.Dentistry journal · 2024Review
- A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System.Journal of imaging informatics in medicine · 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
- Advanced deep learning techniques for recognition of dental implants.Journal of oral biology and craniofacial researchArticle
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1 author.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to evaluate the accuracy and clinical usability of implant system classification using automated machine learning on a Google Cloud platform.
methodsFour dental implant systems were selected: Osstem TSIII, Osstem USII, Biomet 3i Os-seotite External, and Dentsply Sirona Xive. A total of 4,800 periapical radiographs (1,200 for each implant system) were collected and labeled based on electronic medical records. Regions of interest were manually cropped to 400×800 pixels, and all images were uploaded to Google Cloud storage. Approximately 80% of the images were used for training, 10% for validation, and 10% for testing. Google automated machine learning (AutoML) Vision automatically executed a neural architecture search technology to apply an appropriate algorithm to the uploaded data. A single-label image classification model was trained using AutoML. The performance of the mod-el was evaluated in terms of accuracy, precision, recall, specificity, and F1 score.
resultsThe accuracy, precision, recall, specificity, and F1 score of the AutoML Vision model were 0.981, 0.963, 0.961, 0.985, and 0.962, respectively. Osstem TSIII had an accuracy of 100%. Osstem USII and 3i Osseotite External were most often confused in the confusion matrix.
conclusionDeep learning-based AutoML on a cloud platform showed high accuracy in the classification of dental implant systems as a fine-tuned convolutional neural network. Higher-quality images from various implant systems will be required to improve the performance and clinical usability of the model.
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