ArticleScientific reports2022
Performance comparison of three deep learning models for impacted mesiodens detection on periapical radiographs.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Using of artificial intelligence to evaluate image quality for interproximal bone level assessment on bitewing radiographs.Imaging science in dentistry · 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 for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- Integrating image processing and deep learning framework for automatic supernumerary tooth detection on occlusal radiographs.Journal of dental sciences · 2026Article
- Customized GPT-4V(ision) for radiographic diagnosis: can large language model detect supernumerary teeth?BMC oral health · 2025Article
- The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review.Oral and maxillofacial surgery · 2025Review
- Validation of an Artificial Intelligence-Based Model for Early Childhood Caries Detection in Dental Photographs.Journal of clinical medicine · 2024Article
- The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs.The Saudi dental journal · 2024Review
- Automatic diagnosis of true proximity between the mandibular canal and the third molar on panoramic radiographs using deep learning.Scientific reports · 2023Article
- Artificial intelligence in diagnosing dens evaginatus on periapical radiography with limited data availability.Scientific reports · 2023Article
- Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Automatic diagnosis of retention pseudocyst in the maxillary sinus on panoramic radiographs using a convolutional neural network algorithm.Scientific reports · 2023Article
- Orthodontic-Surgical Management of Triple Impacted Mesiodens and Maxillary Incisors - A Case Report.Annals of maxillofacial surgeryArticle
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop deep learning models that automatically detect impacted mesiodens on periapical radiographs of primary and mixed dentition using the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms and to compare their performance. Periapical radiographs of 600 pediatric patients (age range, 3-13 years) with mesiodens were used as a training and validation dataset. Deep learning models based on the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms for detecting mesiodens were developed, and each model was trained 300 times using training (540 images) and validation datasets (60 images). The performance of each model was evaluated based on accuracy, sensitivity, and specificity using 120 test images (60 periapical radiographs with mesiodens and 60 periapical radiographs without mesiodens). The accuracy of the YOLOv3, RetinaNet, and EfficientDet-D3 models was 97.5%, 98.3%, and 99.2%, respectively. The sensitivity was 100% for both the YOLOv3 and RetinaNet models and 98.3% for the EfficientDet-D3 model. The specificity was 100%, 96.7%, and 95.0% for the EfficientDet-D3, RetinaNet, and YOLOv3 models, respectively. The proposed models using three deep learning algorithms to detect mesiodens on periapical radiographs showed good performance. The EfficientDet-D3 model showed the highest accuracy for detecting mesiodens on periapical radiographs.
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