ArticleScientific reports2024
Identification of dental implant systems from low-quality and distorted dental radiographs using AI trained on a large multi-center dataset.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Deep Learning-Based Automated Diagnostic Charting on Panoramic Radiography: Comparison of YOLOv11 and YOLOv12.Odontology · 2026Article
- Reliability of the QUADAS-2 checklist in AI-based medical imaging: A comparison of human reviewers and ChatGPT-4o.Imaging science in dentistry · 2026Article
- Classification of dental implants using supervised deep learning from dental radiographs: A scoping review.Imaging science in dentistry · 2026Review
- The Use of Artificial Intelligence in Planning Dental Implant Procedures: A Systematic Review.Dentistry journal · 2026Review
- Present and Future of the Use of Artificial Intelligence in Orthodontics.Bioengineering (Basel, Switzerland) · 2026Review
- Automated assessment of peri-implant disease severity by deep learning and image processing in periapical radiographs.Journal of dental sciences · 2026Article
- Customized Closed-Tray Impression Post for Unknown Implant System: Case Report.Case reports in dentistry · 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
- Evaluating the quality and empathy of responses to patient questions on the Korean Academy of Periodontology's online question and answer section: a cross-sectional study comparing periodontists and an AI-powered chatbot.Journal of periodontal & implant science · 2025Article
- Performance of artificial intelligence-based diagnosis and classification of peri-implantitis compared with periodontal surgeon assessment: a pilot study of panoramic radiograph analysis.Journal of periodontal & implant science · 2025Article
- Efficacy of deep learning models and dental professionals in identifying dental implants.Imaging science in dentistry · 2025Article
- Hybrid Faster R-CNN for Tooth Numbering in Periapical Radiographs Based on Fédération Dentaire Internationale System.Diagnostics (Basel, Switzerland) · 2025Article
- Comparative analysis of YOLO variants for dental implant brand identification.Scientific reports · 2025Article
- A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs.Journal of imaging informatics in medicine · 2025Article
- The Use of Artificial Intelligence in Caries Detection: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Materials and Techniques in Dentistry, Oral Surgery and Orthodontics.Materials (Basel, Switzerland) · 2024Article
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3 authors.
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Abstract
Most artificial intelligence (AI) studies have attempted to identify dental implant systems (DISs) while excluding low-quality and distorted dental radiographs, limiting their actual clinical use. This study aimed to evaluate the effectiveness of an AI model, trained on a large and multi-center dataset, in identifying different types of DIS in low-quality and distorted dental radiographs. Based on the fine-tuned pre-trained ResNet-50 algorithm, 156,965 panoramic and periapical radiological images were used as training and validation datasets, and 530 low-quality and distorted images of four types (including those not perpendicular to the axis of the fixture, radiation overexposure, cut off the apex of the fixture, and containing foreign bodies) were used as test datasets. Moreover, the accuracy performance of low-quality and distorted DIS classification was compared using AI and five periodontists. Based on a test dataset, the performance evaluation of the AI model achieved accuracy, precision, recall, and F1 score metrics of 95.05%, 95.91%, 92.49%, and 94.17%, respectively. However, five periodontists performed the classification of nine types of DISs based on four different types of low-quality and distorted radiographs, achieving a mean overall accuracy of 37.2 ± 29.0%. Within the limitations of this study, AI demonstrated superior accuracy in identifying DIS from low-quality or distorted radiographs, outperforming dental professionals in classification tasks. However, for actual clinical application of AI, extensive standardization research on low-quality and distorted radiographic images is essential.
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