ArticleOral radiology2021
Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data.
Article in Oral radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 123 citations in OpenAlex.
- Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis.Dento maxillo facial radiology · 2024Pooled it
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Tooth Detection and Numbering Performance of Two AI-Based Systems on Panoramic Radiographs: A Comparative Study.Dentistry journal · 2026Article
- Impact of image processing techniques on deep learning-based classification accuracy of cervical vertebral maturation.Oral radiology · 2026Article
- Advanced deep learning techniques for classifying dental conditions using panoramic X-ray images.BMC oral health · 2026Article
- Advanced Deep Learning Models for Classifying Dental Diseases from Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- How artificial intelligence is reshaping citation impact in oral and maxillofacial radiology journals: an 8-year analysis with editorial and clinical implications (JCR 2017-2024).Frontiers in research metrics and analytics · 2026Article
- AI-powered precision in dental radiographic analysis using tailored CNNs for tooth numbering and cavity detection.PLOS digital health · 2025Article
- Multi-label diagnosis of dental conditions from panoramic x-rays using attention-enhanced deep learning.Oral and maxillofacial surgery · 2025Article
- DenPAR: Annotated Intra-Oral Periapical Radiographs Dataset for Machine Learning.Scientific data · 2025Article
- Deep learning-based object detection algorithms in medical imaging: Systematic review.Heliyon · 2025Review
- Deep learning with convolution neural network detecting mesiodens on panoramic radiographs: comparing four models.Odontology · 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
- Orthopantomogram teeth segmentation and numbering dataset.Data in brief · 2024Article
- Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Tooth numbering with polygonal segmentation on periapical radiographs: an artificial intelligence study.Clinical oral investigations · 2024Article
- YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.BMC medical imaging · 2024Article
- A Multi-center Dental Panoramic Radiography Image Dataset for Impacted Teeth, Periodontitis, and Dental Caries: Benchmarking Segmentation and Classification Tasks.Journal of imaging informatics in medicine · 2024Article
- Deep learning and artificial intelligence in dental diagnostic imaging.The Japanese dental science review · 2023Review
- Effect of data size on tooth numbering performance via artificial intelligence using panoramic radiographs.Oral radiology · 2023Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesDental state plays an important role in forensic radiology in case of large scale disasters. However, dental information stored in dental clinics are not standardized or electronically filed in general. The purpose of this study is to develop a computerized system to detect and classify teeth in dental panoramic radiographs for automatic structured filing of the dental charts. It can also be used as a preprocessing step for computerized image analysis of dental diseases.
methodsOne hundred dental panoramic radiographs were employed for training and testing an object detection network using fourfold cross-validation method. The detected bounding boxes were then classified into four tooth types, including incisors, canines, premolars, and molars, and three tooth conditions, including nonmetal restored, partially restored, and completely restored, using classification network. Based on the visualization result, multisized image data were used for the double input layers of a convolutional neural network. The result was evaluated by the detection sensitivity, the number of false-positive detection, and classification accuracies.
resultsThe tooth detection sensitivity was 96.4% with 0.5 false positives per case. The classification accuracies for tooth types and tooth conditions were 93.2% and 98.0%. Using the double input layer network, 6 point increase in classification accuracy was achieved for the tooth types.
conclusionsThe proposed method can be useful in automatic filing of dental charts for forensic identification and preprocessing of dental disease prescreening purposes.
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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.