AI Framework for Dental Shade Matching Under Variable Lighting Conditions.

BACKGROUND: Accurate tooth shade matching remains challenging because visual assessment is subjective and commercial spectrophotometers are sensitive to clinical conditions. Although artificial intelligence (AI) offers a promising alternative, most AI-based methods have been developed and tested under controlled imaging conditions, with limited evidence in routine practice. Herein, we developed PerceptShade, an AI framework for image-based shade estimation from routine intraoral photographs captured under variable lighting and with diverse devices, including smartphones and digital cameras. METHODS: PerceptShade incorporates clinical perceptual principles by focusing on non-specular tooth regions, enforcing a brightness-first hierarchical judgment inspired by standard clinical shade-matching protocols, and learning perceptual similarity through Perceptual Consistency Optimization. The framework was trained and evaluated using 1,553 shade-matching images from 357 patients, with 50 prospectively collected independent cases used for an observer study. The dataset included heterogeneous lighting and device conditions to reflect routine clinical conditions. RESULTS: PerceptShade achieved Top-1 and Top-3 accuracies of 79.12% and 96.44%, respectively, in the shade-matching test. In the independent observer dataset, PerceptShade achieved a Top-1 agreement of 76.0% with the Panel A consensus reference (95%CI: 61.8-86.9%), compared with 72.7% among independent expert observers (95%CI: 65.1%-80.3%). These Top-N recommendations may streamline digital shade-matching workflows and support clinical decision-making. CONCLUSION: PerceptShade is a novel framework integrating hierarchical clinical reasoning with illumination-robust representation learning for dental shade matching. In this image-based evaluation, it demonstrated stable performance across heterogeneous acquisition conditions and may support shade selection in routine clinical photography. CLINICAL SIGNIFICANCE: PerceptShade is an AI-based dental shade-matching framework inspired by clinicians' perceptual workflow. Using routine photographs acquired without device calibration, it provides shade recommendations for single-tooth restorations. It may support candidate-shade selection, although it requires an in-frame reference tab and may still be affected by challenging imaging conditions.