Decision support system for predicting color change after tooth whitening

Area of Science:

• Dentistry
• Biomedical Engineering
• Color Science

Background:

• Tooth whitening is a popular, minimally invasive cosmetic dental procedure.
• Predicting the exact color change after whitening can be challenging, leading to variability in outcomes.
• A reliable method for predicting whitening results is needed to manage patient expectations and optimize treatments.

Purpose of the Study:

• To develop a clinical decision support system (CDSS) for predicting color change following in-office tooth whitening.
• To utilize patient data and colorimetric measurements to create a predictive model.
• To evaluate the accuracy of the developed CDSS in predicting post-treatment tooth color.

Main Methods:

• Collected patient datasets including original tooth color (CIELAB L*, a*, b* values).
• Developed a multiple regression equation incorporating initial color and color difference (ΔE) to predict post-whitening color.
• Validated the system by comparing its predictions against actual post-treatment tooth colors (gold standard).

Main Results:

• The developed CDSS demonstrated a high degree of agreement with actual patient post-treatment colors.
• The agreement between predicted and actual colors was quantified with a kappa value of 0.894.
• The system successfully predicted color changes achieved with an in-office whitening system using colorimetric data.

Conclusions:

• A clinical decision support system can effectively predict tooth color changes after in-office whitening.
• The use of CIELAB color coordinates and multiple regression analysis provides a reliable predictive model.
• This system has the potential to improve treatment planning and patient satisfaction in cosmetic dentistry.