Synthetic imaging in dentistry: A narrative review of deep learning techniques and applications
Abstract
OBJECTIVE: Progress in the development of deep learning tools for dental imaging is constrained by limited access to real-world datasets due to privacy concerns, class imbalance, and data scarcity. This narrative review focuses on the use of synthetic data as a potential solution to these challenges. DATA AND SOURCES: The review addresses both technical, clinical, and ethical/regulatory aspects, and was drafted by a multidisciplinary team. Each subsection was assigned to at least two contributors, with two central members overseeing the entire process. Relevant studies were identified through electronic searches in PubMed, Scopus, Embase, Google Scholar, Web of Science, and IEEE Xplore, supplemented by conference papers and book chapters. For the subsection on clinical applications, publications in the domain of dentistry and oral health focused on fully synthetic image generation were included; studies on image translation or other image processing tasks were excluded. CONCLUSION: Synthetic imaging data can be generated using generative adversarial networks, variational autoencoders, and denoising diffusion probabilistic models. Synthetic imaging can complement real-world data by mitigating class imbalance, augmenting scarce datasets, and enabling diverse, realistic representations of rare conditions and anatomical variations. It holds promise for diagnostics, education, and multimodal integration across imaging modalities. Studies on dental image synthesis remain scarce,…
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.