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Related Experiment Video

Updated: May 15, 2026

A Standardized Approach to Extra-Oral and Intra-Oral Digital Photography
06:49

A Standardized Approach to Extra-Oral and Intra-Oral Digital Photography

Published on: July 22, 2022

AI framework for dental shade matching under variable lighting conditions.

Ran Tao1, Hao Feng2, Peixi Liao3

  • 1College of Computer Science, Sichuan University, Chengdu 610065, China.

Journal of Dentistry
|May 13, 2026
PubMed
Summary

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PerceptShade, an AI framework, accurately estimates tooth shades from routine intraoral photos, overcoming challenges of subjective visual assessment and device variability. This technology aids dental professionals in shade selection for restorations.

Area of Science:

  • Artificial Intelligence in Dentistry
  • Digital Dentistry
  • Image Analysis for Dental Applications

Background:

  • Accurate tooth shade matching is crucial for aesthetic dental restorations but remains challenging due to subjective visual assessment and spectrophotometer sensitivity.
  • Existing AI methods for shade matching often require controlled imaging conditions, limiting their applicability in routine clinical practice.
  • PerceptShade, an AI framework, addresses these limitations by enabling image-based shade estimation from routine intraoral photographs captured under variable conditions.

Purpose of the Study:

  • To develop and evaluate PerceptShade, an AI framework for accurate dental shade estimation using routine intraoral photographs.
  • To assess the performance of PerceptShade under diverse lighting and device conditions, reflecting real-world clinical scenarios.
  • To compare the AI's shade-matching accuracy with that of expert human observers.
Keywords:
ColorimetryDeep learningImage interpretation, computer-assistedPhotography, dentalProsthesis coloring

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Main Methods:

  • PerceptShade integrates clinical perceptual principles, focusing on non-specular tooth regions and employing a brightness-first hierarchical judgment.
  • The framework utilizes Perceptual Consistency Optimization to learn perceptual similarity from 1,553 shade-matching images across 357 patients.
  • Performance was validated using 50 independent cases and compared against expert consensus in an observer study.

Main Results:

  • PerceptShade achieved Top-1 and Top-3 accuracies of 79.12% and 96.44% in shade-matching tests.
  • In an independent observer study, PerceptShade demonstrated a Top-1 agreement of 76.0% with the consensus reference, outperforming expert observers (72.7%).
  • The AI framework showed stable performance across heterogeneous acquisition conditions, including variable lighting and diverse devices like smartphones.

Conclusions:

  • PerceptShade is a novel AI framework for dental shade matching, integrating clinical reasoning with robust representation learning.
  • The framework demonstrated reliable performance in image-based shade evaluation under routine clinical conditions.
  • PerceptShade shows potential to streamline digital shade-matching workflows and support clinical decision-making for single-tooth restorations.