ArticleScientific reports2021
Automatic detection of mesiodens on panoramic radiographs using artificial intelligence.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating YOLO for dental caries diagnosis: a systematic review and meta-analysis.Evidence-based dentistry · 2025Pooled it
- Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Automated Cephalometric Points Marking System.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review.Biomimetics (Basel, Switzerland) · 2026Review
- Application of Artificial Intelligence in Detecting Dental Anomalies: Current Models, Imaging Modalities, and Future Directions.Health science reports · 2026Article
- Generalizability of YOLOv11 models for mesiodens detection in pediatric panoramic radiographs.BMC oral health · 2026Article
- Artificial Intelligence in Pediatric Dentistry: A Systematic Review and Meta-Analysis.Children (Basel, Switzerland) · 2026Review
- AI-Based Intraoral Videography for Automated Dental Inspection and Charting in Children With Mixed Dentition.International journal of telemedicine and applications · 2026Article
- Diagnostic accuracy of an artificial intelligence-based software in detecting supernumerary and congenitally missing teeth in panoramic radiographs.European journal of orthodontics · 2025Article
- Automated quality evaluation of dental panoramic radiographs using deep learning.Imaging science in dentistry · 2025Article
- Customized GPT-4V(ision) for radiographic diagnosis: can large language model detect supernumerary teeth?BMC oral health · 2025Article
- Evaluation of the Performance of a YOLOv10-Based Deep Learning Model for Tooth Detection and Numbering on Panoramic Radiographs of Patients in the Mixed Dentition Period.Diagnostics (Basel, Switzerland) · 2025Article
- The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review.Oral and maxillofacial surgery · 2025Review
- Accuracy of AI-based binary classification for detecting malocclusion in the mixed dentition stage.PloS one · 2025Article
- Style harmonization of panoramic radiography using deep learning.Oral radiology · 2025Article
- Fusing global context with multiscale context for enhanced breast cancer classification.Scientific reports · 2024Article
- Review
- YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.BMC medical imaging · 2024Article
- The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature.Healthcare (Basel, Switzerland) · 2024Article
- Perceptions and attitudes of dental students and dentists in South Korea toward artificial intelligence: a subgroup analysis based on professional seniority.BMC medical education · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop an artificial intelligence model that can detect mesiodens on panoramic radiographs of various dentition groups. Panoramic radiographs of 612 patients were used for training. A convolutional neural network (CNN) model based on YOLOv3 for detecting mesiodens was developed. The model performance according to three dentition groups (primary, mixed, and permanent dentition) was evaluated, both internally (130 images) and externally (118 images), using a multi-center dataset. To investigate the effect of image preprocessing, contrast-limited histogram equalization (CLAHE) was applied to the original images. The accuracy of the internal test dataset was 96.2% and that of the external test dataset was 89.8% in the original images. For the primary, mixed, and permanent dentition, the accuracy of the internal test dataset was 96.7%, 97.5%, and 93.3%, respectively, and the accuracy of the external test dataset was 86.7%, 95.3%, and 86.7%, respectively. The CLAHE images yielded less accurate results than the original images in both test datasets. The proposed model showed good performance in the internal and external test datasets and had the potential for clinical use to detect mesiodens on panoramic radiographs of all dentition types. The CLAHE preprocessing had a negligible effect on model performance.
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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.