Synthetic imaging in dentistry: A narrative review of deep learning techniques and applications
Bibliographic record
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, and comprehensive evidence regarding the impact of data augmentation using synthetic images is lacking. Key challenges persist, including ensuring anatomical fidelity and minimizing artifacts. Future emphasis should be on interdisciplinary collaboration, standardized generation workflows, open-source tools, robust strategies for synthetic data integration, and clear regulatory guidance. CLINICAL SIGNIFICANCE: Synthetic imaging can help overcome data scarcity and class imbalance in dental artificial intelligence (AI), leading to more robust and generalizable AI models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".