UNIVARIATE CLASSIFICATION OF DEGRADED CARTILAGE SAMPLES USING MRI PARAMETERS

Abstract

Early diagnosis of osteoarthritis is important in implementing treatments to control the progression of the disease. In this thesis, we applied univariate classification methods to distinguish healthy and degraded cartilage samples using five MRI parameters. We first replicated the univariate classification method used in the industry. We then tried to improve specificity and sensitivity by introducing new classification methods: standardized distance, likelihood ratio test, and likelihood ratio test with noise added to the validation set. We applied the conventional and the new classification methods to control, 18-h trypsin-digested, and 20-h collagenase-digested samples. Two separate analyses were performed; control group combined with trypsin-digested group and the same control group combined with collagenase-digested group. Specificity and sensitivity were computed and the results of all four methods were compared. Furthermore, a simulation analysis was done to compare the accuracy rate of the conventional method and the likelihood ratio test.