ArticleDento maxillo facial radiology2023
Refinement of image quality in panoramic radiography using a generative adversarial network.
Article in Dento maxillo facial radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2026Pooled it
- Assessment of artificial intelligence in detecting errors on panoramic radiographs.Imaging science in dentistry · 2026Article
- Artificial Intelligence-Related Dental Research: Bibliometric and Altmetric Analysis.International dental journal · 2025Article
- Style harmonization of panoramic radiography using deep learning.Oral radiology · 2025Article
- Article
- Generative Adversarial Networks (GANs) in the Field of Head and Neck Surgery: Current Evidence and Prospects for the Future-A Systematic Review.Journal of clinical medicine · 2024Review
- Attention-guided jaw bone lesion diagnosis in panoramic radiography using minimal labeling effort.Scientific reports · 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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe aimed to develop and assess the clinical usefulness of a generative adversarial network (GAN) model for improving image quality in panoramic radiography.
methodsPanoramic radiographs obtained at Yonsei University Dental Hospital were randomly selected for study inclusion (
resultsAmong the degraded images, the GAN model enabled the greatest improvement in those with blur in the region of the anterior teeth but was least effective in improving images exhibiting blur with noise (PSNR, 36.27 > 32.74; SSIM, 0.90 > 0.82). While the mean clinical image quality score of the original radiographs was 44.6 out of 46.0, the highest and lowest predicted scores were observed in the blur (45.2) and noise (36.0) groups.
conclusionThe GAN model developed in this study has the potential to improve panoramic radiographs with degraded image quality, both quantitatively and qualitatively. As the model performs better in refining blurred images, further research is required to identify the most effective methods for handling noisy images.
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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.