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Related Experiment Video

Updated: Jun 11, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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A machine learning based approach to standardizing tooth color and shade recommendations.

Qijing Li1, Du Chen2, Hang Wang3

  • 1Master's student, State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, PR China.

The Journal of Prosthetic Dentistry
|October 1, 2024
PubMed
Summary

This study introduces a two-tier color correction strategy using machine learning (ML) to improve dental shade matching. The extreme learning machine (ELM) model achieved high accuracy, significantly reducing color differences for esthetic restorations.

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Area of Science:

  • * Dental materials science and digital dentistry.
  • * Application of artificial intelligence in esthetic dentistry.

Background:

  • * Precise tooth color reproduction is crucial for esthetic dental restorations.
  • * Current visual and instrumental shade matching methods have limitations, leading to inconsistent color communication and suboptimal esthetic outcomes.
  • * Developing standardized and accurate methods for dental color acquisition is essential.

Purpose of the Study:

  • * To evaluate a two-tier color correction strategy for accurate, standardized tooth color acquisition.
  • * To provide reliable shade recommendations for esthetic dental restorations.
  • * To assess the efficacy of machine learning models in dental color correction.

Main Methods:

  • * Machine learning (ML) models, including extreme learning machine (ELM), were trained using color values from standardized photographs of a color card and shade guide.
  • * A two-tier color correction process was applied to clinical photographs using ML models and the VITA 3D-Master color space.
  • * Prediction accuracy and color correction precision were evaluated using RMSE, R², and color difference metrics.

Main Results:

  • * The ELM model demonstrated superior performance with the lowest RMSE (2.2) and highest R² (0.996).
  • * The two-tier color correction reduced color difference from 7.6 to 1.0, falling below acceptability thresholds.
  • * Shade recommendations achieved 73.1% accuracy when compared to the ground truth of shade guides.

Conclusions:

  • * The proposed two-tier color correction strategy effectively standardizes dental photograph color.
  • * This ML-based approach offers a more accurate and stable method for color communication between dentists and dental technicians.
  • * The strategy enhances the precision of shade recommendations for esthetic dental restorations.