Imaging science in dentistry · 2026
Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.
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Hwang Jae Joon, Kim Na-Yeon, Jung Yun-Hoa, Cho Bong-Hae, Heo Min-Suk
Аннотация
PURPOSE: This narrative review examined whether primary dental and maxillofacial imaging studies support, qualify, or counter the conclusions of previous systematic reviews of transformer and generative models, with findings assessed by topic rather than by review.
MATERIALS AND METHODS: Structured PubMed searches covering 2019-2026 retained 159 records, and supplementary searches addressed topic-specific gaps. Dental and maxillofacial evidence was distinguished from medical transfer evidence. Thirty-six comparisons were then assessed for external validation, patient separation, comparator fairness, and clinical endpoints.
RESULTS: In task-level architecture comparisons, previous reviews reported frequent first-place rankings for transformer models but limited external validation. Primary studies qualified that pattern: convolutional neural networks won the largest matched segmentation comparison, Swin-Unet rankings reversed across datasets, convolutional models led two direct cone-beam computed tomography comparisons, and one transformer retained high performance in external surface segmentation. Of 36 comparisons, 4 used independent external validation, 15 had a fair same-data comparator, and 15 included a diagnostic, reader, or clinical endpoint. Generative reviews reported image-quality gains, but heterogeneity exceeded 99% in a cone-beam computed tomography meta-analysis, and most dental metal-artifact studies had a high risk of bias. Crack visibility improved ex vivo; low-dose cone-beam computed tomography improved structure visibility without significantly changing treatment or referral decisions.
CONCLUSION: Primary evidence supports complementarity between convolutional neural networks and transformers and suggests early clinical translation, but it does not support architecture-wide transformer superiority. The directly observed gaps were matched comparisons, independent external testing, use-specific endpoints, and measurement of anatomy removed or introduced by pixel-modifying models.
MATERIALS AND METHODS: Structured PubMed searches covering 2019-2026 retained 159 records, and supplementary searches addressed topic-specific gaps. Dental and maxillofacial evidence was distinguished from medical transfer evidence. Thirty-six comparisons were then assessed for external validation, patient separation, comparator fairness, and clinical endpoints.
RESULTS: In task-level architecture comparisons, previous reviews reported frequent first-place rankings for transformer models but limited external validation. Primary studies qualified that pattern: convolutional neural networks won the largest matched segmentation comparison, Swin-Unet rankings reversed across datasets, convolutional models led two direct cone-beam computed tomography comparisons, and one transformer retained high performance in external surface segmentation. Of 36 comparisons, 4 used independent external validation, 15 had a fair same-data comparator, and 15 included a diagnostic, reader, or clinical endpoint. Generative reviews reported image-quality gains, but heterogeneity exceeded 99% in a cone-beam computed tomography meta-analysis, and most dental metal-artifact studies had a high risk of bias. Crack visibility improved ex vivo; low-dose cone-beam computed tomography improved structure visibility without significantly changing treatment or referral decisions.
CONCLUSION: Primary evidence supports complementarity between convolutional neural networks and transformers and suggests early clinical translation, but it does not support architecture-wide transformer superiority. The directly observed gaps were matched comparisons, independent external testing, use-specific endpoints, and measurement of anatomy removed or introduced by pixel-modifying models.