ArticleDiagnostics (Basel, Switzerland)2021
Automated Mesiodens Classification System Using Deep Learning on Panoramic Radiographs of Children.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Automated detection of periapical lesions in pediatric panoramic radiographs using YOLOv7: a retrospective internal validation study.BMC pediatrics · 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
- Integrating image processing and deep learning framework for automatic supernumerary tooth detection on occlusal radiographs.Journal of dental sciences · 2026Article
- Deep Spectrogram Learning for Gunshot Classification: A Comparative Study of CNN Architectures and Time-Frequency Representations.Journal of imaging · 2025Article
- Diagnostic accuracy of an artificial intelligence-based software in detecting supernumerary and congenitally missing teeth in panoramic radiographs.European journal of orthodontics · 2025Article
- The Validation of an Artificial Intelligence-Based Software for the Detection and Numbering of Primary Teeth on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence-based detection of dens invaginatus in panoramic radiographs.BMC oral health · 2025Article
- Customized GPT-4V(ision) for radiographic diagnosis: can large language model detect supernumerary teeth?BMC oral health · 2025Article
- Improving TMJ Diagnosis: A Deep Learning Approach for Detecting Mandibular Condyle Bone Changes.Diagnostics (Basel, Switzerland) · 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
- Deep learning-based automated detection of supernumerary teeth in pediatric panoramic radiographs.PloS one · 2025Article
- The role of artificial intelligence in diagnosing pediatric dental disorders-a narrative review.Frontiers in dental medicine · 2025Review
- Using AI in Optimizing Oral and Dental Diagnoses-A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Validation of an Artificial Intelligence-Based Model for Early Childhood Caries Detection in Dental Photographs.Journal of clinical medicine · 2024Article
- Review
- YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.BMC medical imaging · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In this study, we aimed to develop and evaluate the performance of deep-learning models that automatically classify mesiodens in primary or mixed dentition panoramic radiographs. Panoramic radiographs of 550 patients with mesiodens and 550 patients without mesiodens were used. Primary or mixed dentition patients were included. SqueezeNet, ResNet-18, ResNet-101, and Inception-ResNet-V2 were each used to create deep-learning models. The accuracy, precision, recall, and F1 score of ResNet-101 and Inception-ResNet-V2 were higher than 90%. SqueezeNet exhibited relatively inferior results. In addition, we attempted to visualize the models using a class activation map. In images with mesiodens, the deep-learning models focused on the actual locations of the mesiodens in many cases. Deep-learning technologies may help clinicians with insufficient clinical experience in more accurate and faster diagnosis.
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Registered trials
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