ArticleFrontiers in artificial intelligence2026
Automated evaluation of dental cavity preparation quality using deep learning and anatomically informed geometric analysis.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The quality of cavity preparation critically influences the longevity and success of restorative dental treatments. Current assessment methods remain largely subjective, relying on visual inspection and examiner judgment, which are prone to variability and limited reproducibility. Although three-dimensional (3D) imaging enables quantitative evaluation, its routine use in clinical and educational settings is limited by cost, accessibility, and workflow complexity. Objective: This study aimed to develop an automated, objective, and clinically interpretable framework for evaluating dental cavity preparation quality using standard two-dimensional (2D) images, with optional integration of 3D depth information. Methods: A deep learning pipeline based on enhanced U-Net architectures was developed to automatically segment cavity and cusp regions from 2D molar photographs. Anatomically informed geometric analyses were applied to quantify cavity-shape similarity, intercuspal distance, isthmus width, and cavity proportionality. Global cavity-shape conformity was assessed using Elliptic Fourier Descriptors (EFDs), enabling scale-, rotation-, and translation-invariant comparisons with reference preparations. When 3D STL data were available, cavity depth and cavity-bed smoothness were additionally quantified. These measurements were integrated into a transparent Cavity Quality Score (CQS) ranging from 1 to 10. Results: The cavity segmentation model achieved an internal validation Dice coefficient of 0.81 and an Intersection-over-Union of 0.74, while cusp segmentation achieved a Dice coefficient of 0.83. External validation using measurements from three independent experts demonstrated close agreement between automated predictions and expert consensus for EFD cavity-shape similarity (MAE = 1.32 percentage points; Conclusion: This study presents a hybrid, explainable artificial intelligence framework for objective assessment of dental cavity preparation using widely available 2D images. By integrating deep learning with anatomically informed geometric analysis, the proposed CQS offers a transparent and scalable tool for formative feedback in clinical and competency-based dental education. Further validation against expert summative grading is required before high-stakes implementation.
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