ArticleScientific reports2025
End-to-end CNN-based detection of permanent first molars and prediction of root development stages from panoramic radiographs.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Comparative Analysis of Third Molar Segmentation Performance Across Dental Developmental Stages and the 18-Year Age Threshold Using Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Article
- First-stage assessment of AI-based anatomical measurement accuracy for the Cameriere European dental age estimation method.International journal of legal medicine · 2026Article
- Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review.Biomimetics (Basel, Switzerland) · 2026Review
- From intelligent models to clinical tools: the evolving landscape of AI in medical imaging.Scientific reports · 2026Article
- Artificial Intelligence in Pediatric Dentistry: A Systematic Review and Meta-Analysis.Children (Basel, Switzerland) · 2026Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to develop a convolutional neural network (CNN)-based end-to-end learning architecture to predict the root development stages of permanent first molar teeth using panoramic radiographs. A dataset of 1629 first molar images was labeled according to the Cvek classification and organized into five subsets (DB-1 to DB-5) based on root development stages and apical foramen status. Teeth patches were cropped using the YOLO approach, and stage prediction was performed with VGG-19, InceptionV3, and EfficientNet-B3 models optimized with the Adamax optimizer at a learning rate of
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Registered trials
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