SynthesisOral radiology2024
Generative adversarial networks in dental imaging: a systematic review.
Synthesis in Oral radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Artificial Intelligence (AI) in Computer-Aided Design (CAD): How AI Is Redefining Dental CAD.Cureus · 2026Review
- Feasibility of Artificial Intelligence-Processed Low-Dose Cone-Beam Computed Tomography in Dental Imaging.Bioengineering (Basel, Switzerland) · 2026Article
- CBMNet: a dual-attention enhanced ConvNeXt model for accurate G.V. Black type I-III classification in intraoral periapical radiographs.Scientific reports · 2025Article
- DentoMorph-LDMs: diffusion models based on novel adaptive 8-connected gum tissue and deciduous teeth loss for dental image augmentation.Scientific reports · 2025Article
- Artificial Intelligence-Related Dental Research: Bibliometric and Altmetric Analysis.International dental journal · 2025Article
- A two-stage deep-learning model for determination of the contact of mandibular third molars with the mandibular canal on panoramic radiographs.BMC oral health · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
objectivesThis systematic review on generative adversarial network (GAN) architectures for dental image analysis provides a comprehensive overview to readers regarding current GAN trends in dental imagery and potential future applications.
methodsElectronic databases (PubMed/MEDLINE, Scopus, Embase, and Cochrane Library) were searched to identify studies involving GANs for dental image analysis. Eighteen full-text articles describing the applications of GANs in dental imagery were reviewed. Risk of bias and applicability concerns were assessed using the QUADAS-2 tool.
resultsGANs were used for various imaging modalities, including two-dimensional and three-dimensional images. In dental imaging, GANs were utilized for tasks such as artifact reduction, denoising, and super-resolution, domain transfer, image generation for augmentation, outcome prediction, and identification. The generated images were incorporated into tasks such as landmark detection, object detection and classification. Because of heterogeneity among the studies, a meta-analysis could not be conducted. Most studies (72%) had a low risk of bias in all four domains. However, only three (17%) studies had a low risk of applicability concerns.
conclusionsThis extensive analysis of GANs in dental imaging highlighted their broad application potential within the dental field. Future studies should address limitations related to the stability, repeatability, and overall interpretability of GAN architectures. By overcoming these challenges, the applicability of GANs in dentistry can be enhanced, ultimately benefiting the dental field in its use of GANs and artificial intelligence.
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