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Automated evaluation of dental cavity preparation quality using deep learning and anatomically informed geometric analysis
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DOI：10.3389/frai.2026.1884843.png)
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Abstract
En 中文
BackgroundThe 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.ObjectiveThis 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.MethodsA 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.ResultsThe 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; r = 0.981); pooled isthmus-width measurements (MAE = 0.03 mm; r = 0.995); pooled cusp-pair distances (MAE = 0.08 mm; r = 0.999); and cavity depth estimation (absolute error ≈ 0.01 mm).ConclusionThis 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.
Keywords:
artificial intelligence
deep learning
image-based assessment
class I cavity preparation
dental cavity preparation
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