ArticleMedical & biological engineering & computing2022
Deep learning-based dental implant recognition using synthetic X-ray images.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- An augmentation-enhanced transformer model for forensic identification using panoramic dental x-rays.International journal of legal medicine · 2026Article
- Research on biomarkers screening for urinary tract infection and the prediction model based on machine learning algorithms.BMC medical informatics and decision making · 2026Article
- Efficacy of deep learning models and dental professionals in identifying dental implants.Imaging science in dentistry · 2025Article
- Artificial Intelligence-Based Detection and Numbering of Dental Implants on Panoramic Radiographs.Clinical implant dentistry and related research · 2025Article
- Comparing Manual and ChatGPT Deep Research on Systematic Search and Selection in the PubMed Database on the Topic of Dental Implantology.International journal of dentistry · 2025Article
- Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial intelligence in dentistry and dental biomaterials.Frontiers in dental medicine · 2024Review
- Deep learning and clustering approaches for dental implant size classification based on periapical radiographs.Scientific reports · 2023Article
- Artificial intelligence in forensic medicine and forensic dentistry.The Journal of forensic odonto-stomatology · 2023Review
- Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film.Bioengineering (Basel, Switzerland) · 2023Article
- Deep learning-based segmentation of caries, implants, fixed prosthesis, and restorations on bitewing radiographs: A retrospective study.Science progressArticle
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A novel algorithm for generating artificial training samples from triangulated three-dimensional (3D) surface models within the context of dental implant recognition is proposed. The proposed algorithm is based on the calculation of two-dimensional (2D) projections (from a number of different angles) of 3D volumetric representations of computer-aided design (CAD) surface models. A fully convolutional network (FCN) is subsequently trained on the artificially generated X-ray images for the purpose of automatically identifying the connection type associated with a specific dental implant in an actual X-ray image. Semi-automated and fully automated systems are proposed for segmenting questioned dental implants from the background in actual X-ray images. Within the context of the semi-automated system, suitable regions of interest (ROIs), which contain the dental implants, are manually specified. However, as part of the fully automated system, suitable ROIs are automatically detected. It is demonstrated that a segmentation/detection accuracy of 94.0% and a classification/recognition accuracy of 71.7% are attainable within the context of the proposed fully automated system. Since the proposed systems utilise an ensemble of techniques that has not been employed for the purpose of dental implant classification/recognition on any previous occasion, the above-mentioned results are very encouraging.
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