ArticleBioMed research international2021
A Convolutional Neural Network for Automatic Tooth Numbering in Panoramic Images.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review.International dental journal · 2024Pooled it
- Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis.Dento maxillo facial radiology · 2024Pooled it
- Development of an automated YOLO v8-based deep learning teeth numbering model for digital orthopantomogram.Oral radiology · 2026Article
- Impact of image processing techniques on deep learning-based classification accuracy of cervical vertebral maturation.Oral radiology · 2026Article
- AI-powered precision in dental radiographic analysis using tailored CNNs for tooth numbering and cavity detection.PLOS digital health · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Graphical user interface-based convolutional neural network models for detecting nasopalatine duct cysts using panoramic radiography.Scientific reports · 2024Article
- Convolutional neural networks for automated tooth numbering on panoramic radiographs: A scoping review.Imaging science in dentistry · 2023Article
- Deep learning for tooth identification and enumeration in panoramic radiographs.Dental research journal · 2023Article
Corrections and comments
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Analysis of dental radiographs and images is an important and common part of the diagnostic process in daily clinical practice. During the diagnostic process, the dentist must interpret, among others, tooth numbering. This study is aimed at proposing a convolutional neural network (CNN) that performs this task automatically for panoramic radiographs. A total of 8,000 panoramic images were categorized by two experts with more than three years of experience in general dentistry. The neural network consists of two main layers: object detection and classification, which is the support of the previous one and a transfer learning to improve computing time and precision. A Matterport Mask RCNN was employed in the object detection. A ResNet101 was employed in the classification layer. The neural model achieved a total loss of 6.17% (accuracy of 93.83%). The architecture of the model achieved an accuracy of 99.24% in tooth detection and 93.83% in numbering teeth with different oral health conditions.
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Registered trials
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