A Digital Shade-Matching Device for Dental Color Determination Using the Support Vector Machine Algorithm

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 9; p. 3051
• Main Authors: Kim, Minah, Kim, Byungyeon, Park, Byungjun, Lee, Minsuk, Won, Youngjae, Kim, Choul-Young, Lee, Seungrag
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12-09-2018; MDPI
• Subjects: Accuracy; Algorithms; Cameras; Classification; Color matching; dental color determination; Dentistry; digital shade-matching device; Failure rates; Hyperplanes; Light emitting diodes; Medical equipment; Robot arms; support vector machine; Support vector machines; Teeth; New York; United States > US; Austria; digital shade-matching device; dental color determination; support vector machine
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18093051

In this study, we developed a digital shade-matching device for dental color determination using the support vector machine (SVM) algorithm. Shade-matching was performed using shade tabs. For the hardware, the typically used intraoral camera was modified to apply the cross-polarization scheme and block the light from outside, which can lead to shade-matching errors. For reliable experiments, a precise robot arm with ±0.1 mm position repeatability and a specially designed jig to fix the position of the VITA 3D-master (3D) shade tabs were used. For consistent color performance, color calibration was performed with five standard colors having color values as the mean color values of the five shade tabs of the 3D. By using the SVM algorithm, hyperplanes and support vectors for 3D shade tabs were obtained with a database organized using five developed devices. Subsequently, shade matching was performed by measuring 3D shade tabs, as opposed to real teeth, with three additional devices. On average, more than 90% matching accuracy and a less than 1% failure rate were achieved with all devices for 10 measurements. In addition, we compared the classification algorithm with other classification algorithms, such as logistic regression, random forest, and k-nearest neighbors, using the leave-pair-out cross-validation method to verify the classification performance of the SVM algorithm. Our proposed scheme can be an optimum solution for the quantitative measurement of tooth color with high accuracy.