Quantitative Analysis of Periodontal Disease with Full-mouth Periapical Imaging Features

• Main Authors: Ai-Lin Sun, 孫愛琳
• Other Authors: Yung-Hui Huang
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/16422713367947203352

碩士 === 義守大學 === 資訊工程學系 === 102 === Periodontal disease is a common dental disease. Most people had been negligent getting a thorough dental check up regularly because periodontal disease can not always cause pain. Often due to other reasons for treatment and found that suffering from periodontal disease. The purpose of this study is used of full-mouth periapical image and captures the imaging features by image processing techniques from periodontal disease patients. The use of Pearson, Logistic Regression (LR) and ROC (Receiver Operating Characteristic) curve analysis to explore the relationship among clinical symptoms of periodontal disease, BMI (Body Mass Index), age and the imaging features. The classification performance with important image features assesses by Kappa consistency of statistical methods, thereby to estimate the probability of a patient suffering from periodontal disease. By LR model set cut-off point for the 0.5, and image average cut-off point for the 0.5,75.725 image classification of the two methods, classification of the LR and image average, accuracy and consistency coefficient was ( 99.2% , 0.964 ) and ( 86.8% , 0.55 ). LR represents a good detection capabilities. Suffering from periodontal disease-related parameters for the age, BMI, image entropy, image average, image standard deviation, which is a key feature of average parameters. LR model consistency factor of up to 0.964, represented by LR to predict the outcome of the classification match is almost consistent with the clinical findings, the result of quantitative information could offer doctors a trustable reference to diagnose periodontal disease.