Composite Restoration using Image Recognition for Teeth Shade Matching using Deep Learning
Proceeding of the 2024 5th Asia Service Sciences and Software Engineering Conference
(2024), pp. 118-125
Jericho John O. Almoro
a
,
Francis Dale P. Caon
a
,
Bianca H. Goldman
a
,
Micah Sophia Q. Tan
a
,
John Angelo B. Yap
a
,
Abraham T. Magpantay
a
a FEU Institute of Technology, Manila, Philippines,
Abstract: Dental shade matching for composite restoration to natural teeth color is a crucial aspect of dental treatment as it can significantly impact patient satisfaction and treatment outcomes. However, the subjective nature of manual shade selection often leads to shade mismatch, which leads to failure on the first visit. In addition, intraoral scanners are inaccessible to small enterprises dental clinic in the Philippines due to its unaffordable pricing. To address this problem, this study proposed a mobile application that utilizes image processing and deep learning techniques for objective and consistent dental shade matching. Exploring Convolutional Neural Network (CNN)-based MediaPipe for Facial Landmark Detection and Support Vector Machines (SVMs) to classify dental shades. The SVM model attained an overall accuracy of 68.5% during the experimental results while the implementation using the mobile application obtained an estimate of 90% during the user testing for A1 to A4 color shade. The findings have significant implications for clinical practice, empowering dental professionals with a reliable tool to improve patient care and satisfaction. This study emphasizes the importance of incorporating advanced technology into clinical practice, ultimately improving patient outcomes.