Caries lesion detection tool using near infrared image processing and decision tree learning

Abstract

The population of those who are developing caries lesions are increasing. To aid dental practitioners in detecting and identifying caries lesions that the time needed to observe an active lesion can be shortened and be more objective is a great help in slowing down the increasing rate of dental cases. The use of Near infrared light as a non-ionizing alternative for radiograph has been used in several medical studies. To maximize the use of NIR light, a prototype with image filtering and segmentation process and machine learning program was designed to identify caries lesion severity using the International Caries Classification and Management System (ICCMS) Caries Merged Categories. It uses CART (Classification and Regression Trees) a decision tree algorithm that trains to classify data and uses various classifiers for machine learning and model training. In the study, images with NIR illumination were used to test the performance of the prototype which was assessed by the dental practitioner beforehand. A total of 122 tooth samples were used in the simulation. Twenty percent (20%) of the total samples were classified as R0, 40% as RA, sixteen percent (16%) as RB and twenty-four percent (24%) as RC according to the ICCMS caries categories. The prototype was proven to yield results with a confidence level not less than ninety-five percent (95%). The Study was relevant to the process of immediate and non-ionizing determination of carries lesions and to the developing role of NIR light usage for tooth illumination.