ArticleDento maxillo facial radiology2019
Tooth detection and numbering in panoramic radiographs using convolutional neural networks.
Article in Dento maxillo facial radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 181 papers, 6 of them syntheses that pooled it.
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Who cites it
181 citing papers in PubMed, 6 syntheses or guidelines pooled it, 450 citations in OpenAlex.
- 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
- Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review.International dental journal · 2024Pooled it
- Panoramic imaging errors in machine learning model development: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis.Dento maxillo facial radiology · 2024Pooled it
- The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review.Dento maxillo facial radiology · 2020Pooled it
- Machine learning algorithms enhance the accuracy of radiographic diagnosis of dental caries: a comparative study.Dento maxillo facial radiology · 2025Trial
- Enhanced Tooth Region Detection Using Pretrained Deep Learning Models.International journal of environmental research and public health · 2022Trial
- Development of an automated YOLO v8-based deep learning teeth numbering model for digital orthopantomogram.Oral radiology · 2026Article
- Tooth Detection and Numbering Performance of Two AI-Based Systems on Panoramic Radiographs: A Comparative Study.Dentistry journal · 2026Article
- 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
- Deep learning-enhanced detection of dental restorations and orthodontic appliances in panoramic radiographs using a clinically annotated dataset.Acta odontologica Scandinavica · 2026Article
- Automated forensic human identification on dental panoramic radiographs using transformer-based detection with supervised metric learning.BMC oral health · 2026Article
- Dental image analysis and surgical decision support using modified generative adversarial networks.BMC oral health · 2026Article
- Exploring the landscape of artificial intelligence in dental and maxillofacial radiology: a bibliometric analysis of studies and trends.Annals of medicine and surgery (2012) · 2026Review
- Structure-Guided Tooth Numbering and Lesion Localization in Visible Light Oral Images.Journal of imaging · 2026Article
- The Influence of Panoramic Radiograph Quality on the Accuracy of AI-Based Tooth Detection.Diagnostics (Basel, Switzerland) · 2026Article
- Automated Cephalometric Points Marking System.Diagnostics (Basel, Switzerland) · 2026Article
- MultiDentNet: a unified deep learning framework for multi-class dental condition screening and preliminary oral lesion triage.Scientific reports · 2026Article
- Deep Learning-Based Classification of Zirconia and Metal-Supported Porcelain Fixed Restorations on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
121 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAnalysis of dental radiographs is an important part of the diagnostic process in daily clinical practice. Interpretation by an expert includes teeth detection and numbering. In this project, a novel solution based on convolutional neural networks (CNNs) is proposed that performs this task automatically for panoramic radiographs.
methodsA data set of 1352 randomly chosen panoramic radiographs of adults was used to train the system. The CNN-based architectures for both teeth detection and numbering tasks were analyzed. The teeth detection module processes the radiograph to define the boundaries of each tooth. It is based on the state-of-the-art Faster R-CNN architecture. The teeth numbering module classifies detected teeth images according to the FDI notation. It utilizes the classical VGG-16 CNN together with the heuristic algorithm to improve results according to the rules for spatial arrangement of teeth. A separate testing set of 222 images was used to evaluate the performance of the system and to compare it to the expert level.
resultsFor the teeth detection task, the system achieves the following performance metrics: a sensitivity of 0.9941 and a precision of 0.9945. For teeth numbering, its sensitivity is 0.9800 and specificity is 0.9994. Experts detect teeth with a sensitivity of 0.9980 and a precision of 0.9998. Their sensitivity for tooth numbering is 0.9893 and specificity is 0.9997. The detailed error analysis showed that the developed software system makes errors caused by similar factors as those for experts.
conclusionsThe performance of the proposed computer-aided diagnosis solution is comparable to the level of experts. Based on these findings, the method has the potential for practical application and further evaluation for automated dental radiograph analysis. Computer-aided teeth detection and numbering simplifies the process of filling out digital dental charts. Automation could help to save time and improve the completeness of electronic dental records.
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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.