ArticleOral radiology2023
Deep learning for preliminary profiling of panoramic images.
Article in Oral radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Review and Meta-Analysis on the Accuracy of Artificial Intelligence Algorithms in Individuals Gender Detection Using Orthopantomograms.International dental journal · 2025Pooled it
- Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review.International journal of legal medicine · 2024Pooled it
- Impact of image processing techniques on deep learning-based classification accuracy of cervical vertebral maturation.Oral radiology · 2026Article
- Comparative Analysis of Third Molar Segmentation Performance Between Sexes Using Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Article
- Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning and artificial intelligence in dental diagnostic imaging.The Japanese dental science review · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study explored the feasibility of using deep learning for profiling of panoramic radiographs. STUDY
designPanoramic radiographs of 1000 patients were used. Patients were categorized using seven dental or physical characteristics: age, gender, mixed or permanent dentition, number of presenting teeth, impacted wisdom tooth status, implant status, and prosthetic treatment status. A Neural Network Console (Sony Network Communications Inc., Tokyo, Japan) deep learning system and the VGG-Net deep convolutional neural network were used for classification.
resultsDentition and prosthetic treatment status exhibited classification accuracies of 93.5% and 90.5%, respectively. Tooth number and implant status both exhibited 89.5% classification accuracy; impacted wisdom tooth status exhibited 69.0% classification accuracy. Age and gender exhibited classification accuracies of 56.0% and 75.5%, respectively.
conclusionOur proposed preliminary profiling method may be useful for preliminary interpretation of panoramic images and preprocessing before the application of additional artificial intelligence techniques.
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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.