Matthew G. Keffalas;Timothy J. Mosher;Kenneth L. Urish;David J. Miller
发明人:
Kenneth L. Urish,Matthew G. Keffalas,Timothy J. Mosher,David J. Miller
申请号：
US13691359
公开号：
US20130137962A1
申请日:
2012.11.30
申请国别(地区):
US
年份:
2013
代理人:
摘要：
In this work, a Magnetic Resonance Imaging (MRI)-based automatic classifier was designed to predict changes due to osteoarthritis (OA) years prior to their symptomatic presentation and radiographic detection. For each patient, multiple image texture features were measured from the T2 map of the patella cartilage and the lateral and medial compartments of the femoral condyle. A support vector machine (SVM)-based linear discriminant function was trained to predict health status, as well as the affected knee compartment. Feature selection was integrated into the classifier training to drastically reduce the number of image (biomarker) features without sacrificing classification accuracy. It was found that a dominant knee compartment determined the classification decision for most patients. We demonstrate that the signal texture index (STI) predicts disease progression prior to symptoms or radiographic signs of OA. In symptomatic individuals, the STI correlates with the pain and severity of OA suggesting it is a sensitive measure of the same on T2 Maps. These observed changes localized to one knee compartment demonstrating the method can localize OA to specific regions.
