Artificial Intelligence Techniques for Dental Artifact Suppression in Medical Imaging
Bibliographic record
Abstract
Dental artifacts are among the most significant diagnostic challenges in computed tomography (CT) and magnetic resonance imaging (MRI), as they degrade image quality and compromise clinical interpretation. This systematic review examined 15 original studies, selected according to the PRISMA flow diagram, with the objective of identifying and evaluating artificial intelligence (AI) algorithms developed for the detection and mitigation of dental artifacts. Performance was assessed with respect to both image quality enhancement, measured through quantitative metrics such as PSNR and SSIM, and diagnostic accuracy. The methodological rigor of the included studies was appraised using the Newcastle–Ottawa Scale. The findings reveal an increasing adoption of deep learning methods—including convolutional neural networks (CNNs), U-Net architectures, and Transformer-based models—alongside traditional metal artifact reduction (MAR) techniques. While these approaches show encouraging results, their performance remains heterogeneous and constrained by the limited size of available datasets. Based on the evidence, we propose a preliminary methodological pipeline to integrate advanced AI techniques for artifact removal and subsequent image classification, with the aim of improving both image quality and diagnostic reliability.
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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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".