A morphological index for assessing hip osteoarthritis severity from radiographic images

A new method is proposed for assessing the severity of hip osteoarthritis (OA) based on radiographic hip joint space (HJS) morphology. 64 hips of patients with verified unilateral OA or bilateral OA were studied by digitizing the corresponding pelvic radiographs. Radiographic OA severity was assessed employing the Kellgren and Lawrence (KL) scale. Using custom-developed software, radiographs were enhanced, the margins of both HJSs were outlined, and 64 regions of interest (ROIs), corresponding to the delineated HJSs, were obtained. Employing custom-developed algorithms, an index ("joint space morphological index" - JSMI) evaluating alterations in the shape and size of HJS was introduced, calculated and normalized with respect to each patient's individual anatomy. The JSMI values were used to introduce classification rules concerning the characterization of a hip in accordance with the KL scale. For each patient in the unilateral OA group, the OA severity was expressed as the percentage of the HJS area difference between the patient's osteoarthritic and contralateral normal hip. The per cent HJS area difference and the JSMI values were used in the design of a regression model for providing a quantitative estimation of OA severity. The per cent HJS area difference correlated highly with the pathological JSMI values (r = -0.83, p<0.001). The implementation of the JSMI-based classification rules resulted in high classification accuracies for characterizing hips as normal or osteoarthritic, 90.6% (95% exact confidence interval (CI): 80.7-96.5%), as well as for discriminating among OA severity categories, 91.7% (95% CI: 77.5-98.2%). Additionally, a simplified approach of JSMI calculation is suggested for daily clinical use. These JSMI values (JSMI simplified) were found not to differ significantly from (p>0.05), and to be strongly correlated with (r = 0.96, p<0.001), the corresponding ones obtained by the computerized approach. Additionally, the implementation of classification rules based on JSMI simplified resulted in classification accuracies identical to the corresponding ones obtained for the JSMI-based rules. The proposed method may be utilized for evaluating OA and monitoring OA progression.