ArticleScientific reports2023
Automated deep learning for classification of dental implant radiographs using a large multi-center dataset.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 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
25 citing papers in PubMed, 48 citations in OpenAlex.
- Deep Learning-Based Identification of Dental Implant Systems from Two-Dimensional Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Implant Dentistry: Clinical Validity, Diagnostic Performance, Surgical Planning, and Medico-Legal Implications-A Narrative Review.Dentistry journal · 2026Review
- 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
- Detection and Classification of Peri-Implant Marginal Bone Loss in Cone-Beam Computed Tomography Using a Deep Learning Approach.Clinical and experimental dental research · 2026Article
- A web-based deep learning cascade for automated detection and quantification of marginal bone loss.BMC oral health · 2026Article
- Integrating image processing and deep learning framework for automatic supernumerary tooth detection on occlusal radiographs.Journal of dental sciences · 2026Article
- Evaluation of artificial intelligence based prosthetic innovation on dental implants: A database research.Bioinformation · 2026Article
- Deep learning-assisted, pathogenesis-informed lung histopathology scoring in preclinical mouse models of SARS-CoV-2 and influenza A infection.Frontiers in immunology · 2026Article
- Efficacy of deep learning models and dental professionals in identifying dental implants.Imaging science in dentistry · 2025Article
- Multi-Regional deep learning models for identifying dental restorations and prosthesis in panoramic radiographs.BMC oral health · 2025Article
- Deep learning approach for tooth numbering and restoration detection on pediatric periapical radiographs in mixed dentition.Clinical oral investigations · 2025Article
- Comparative analysis of YOLO variants for dental implant brand identification.Scientific reports · 2025Article
- Exploring the Role of Artificial Intelligence in Dental Implantology: A Scholarly Review.Journal of pharmacy & bioallied sciences · 2025Review
- 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
- Race to the Moon or the Bottom? Applications, Performance, and Ethical Considerations of Artificial Intelligence in Prosthodontics and Implant Dentistry.Dentistry journal · 2024Review
- Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Deep learning to assess bone quality from panoramic radiographs: the feasibility of clinical application through comparison with an implant surgeon and cone-beam computed tomography.Journal of periodontal & implant science · 2024Article
- Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review.Annals of biomedical engineering · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to evaluate the accuracy of automated deep learning (DL) algorithm for identifying and classifying various types of dental implant systems (DIS) using a large-scale multicenter dataset. Dental implant radiographs of pos-implant surgery were collected from five college dental hospitals and 10 private dental clinics, and validated by the National Information Society Agency and the Korean Academy of Oral and Maxillofacial Implantology. The dataset contained a total of 156,965 panoramic and periapical radiographic images and comprised 10 manufacturers and 27 different types of DIS. The accuracy, precision, recall, F1 score, and confusion matrix were calculated to evaluate the classification performance of the automated DL algorithm. The performance metrics of the automated DL based on accuracy, precision, recall, and F1 score for 116,756 panoramic and 40,209 periapical radiographic images were 88.53%, 85.70%, 82.30%, and 84.00%, respectively. Using only panoramic images, the DL algorithm achieved 87.89% accuracy, 85.20% precision, 81.10% recall, and 83.10% F1 score, whereas the corresponding values using only periapical images achieved 86.87% accuracy, 84.40% precision, 81.70% recall, and 83.00% F1 score, respectively. Within the study limitations, automated DL shows a reliable classification accuracy based on large-scale and comprehensive datasets. Moreover, we observed no statistically significant difference in accuracy performance between the panoramic and periapical images. The clinical feasibility of the automated DL algorithm requires further confirmation using additional clinical datasets.
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.