ArticleDiagnostics (Basel, Switzerland)2023
Super-Resolution of Dental Panoramic Radiographs Using Deep Learning: A Pilot Study.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2026Pooled it
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Quantitative and Qualitative Evaluation of a Confidence-Aware Transformer-Based Super-Resolution Framework for Panoramic Radiographs.International dental journal · 2026Article
- Enhancing diagnostic quality in dental bitewings using transformer and GAN-Based image restoration.Oral radiology · 2026Article
- Super-resolution of periapical and bitewing digital radiographs using convolutional neural network.BMC medical imaging · 2026Article
- Deep learning-driven super-resolution for cone-beam computed tomography: AnImaging science in dentistry · 2026Article
- Assessing the diagnostic performance of MediXpar texture analysis for C-shaped canal morphology on panoramic radiographs: a pilot study.Clinical oral investigations · 2026Article
- Can artificial intelligence in orthopantomography advance dental diagnostics through automated image analysis?Frontiers in radiology · 2026Review
- Deep learning for dentomaxillofacial cone-beam computed tomography image quality enhancement: A pilot study.Imaging science in dentistry · 2025Article
- Roles of olive oil and physical exercise in non-alcoholic fatty liver disease after ultrasound-based evaluation.World journal of hepatology · 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
- Improving resolution of panoramic radiographs: super-resolution concept.Dento maxillo facial radiology · 2024Article
- Detecting anteriorly displaced temporomandibular joint discs using super-resolution magnetic resonance imaging: a multi-center study.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Using super-resolution (SR) algorithms, an image with a low resolution can be converted into a high-quality image. Our objective was to compare deep learning-based SR models to a conventional approach for improving the resolution of dental panoramic radiographs. A total of 888 dental panoramic radiographs were obtained. Our study involved five state-of-the-art deep learning-based SR approaches, including SR convolutional neural networks (SRCNN), SR generative adversarial network (SRGAN), U-Net, Swin for image restoration (SwinIr), and local texture estimator (LTE). Their results were compared with one another and with conventional bicubic interpolation. The performance of each model was evaluated using the metrics of mean squared error (MSE), peak signal-to-noise ratio (PNSR), structural similarity index (SSIM), and mean opinion score by four experts (MOS). Among all the models evaluated, the LTE model presented the highest performance, with MSE, SSIM, PSNR, and MOS results of 7.42 ± 0.44, 39.74 ± 0.17, 0.919 ± 0.003, and 3.59 ± 0.54, respectively. Additionally, compared with low-resolution images, the output of all the used approaches showed significant improvements in MOS evaluation. A significant enhancement in the quality of panoramic radiographs can be achieved by SR. The LTE model outperformed the other models.
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