Knee Images Digital Analysis (KlDA): a novel method to quantify individual radiographic features of knee osteoarthritis in detail

Objective: Radiography is still the golden standard for imaging features of osteoarthritis (OA), such as joint space narrowing, subchondral sclerosis, and osteophyte formation. Objective assessment, however, remains difficult. The goal of the present study was to evaluate a novel digital method to analyse standard knee radiographs. Methods: Standardized radiographs of 20 healthy and 55 CA knees were taken in general practise according to the semi-flexed method by Buckland-Wright. Joint Space Width (JSW), osteophyte area, subchondral bone density, joint angle, and tibial eminence height were measured as continuous variables using newly developed Knee Images Digital Analysis (KIDA) software on a standard PC. Two observers evaluated the radiographs twice, each on two different occasions. The observers were blinded to the source of the radiographs and to their previous measurements. Statistical analysis to compare measurements within and between observers was performed according to Bland and Altman. Correlations between KIDA data and Kellgren & Lawrence (K&L) grade were calculated and data of healthy knees were compared to those of CA knees. Results: Intra- and inter-observer variations for measurement of JSW, subchondral bone density, osteophytes, tibial eminence, and joint angle were small. Significant correlations were found between KIDA parameters and K&L grade. Furthermore, significant differences were found between healthy and CA knees. Conclusion: In addition to JSW measurement, objective evaluation of osteophyte formation and subchondral bone density is possible on standard radiographs. The measured differences between CA and healthy individuals suggest that KIDA allows detection of changes in time, although sensitivity to change has to be demonstrated in long-term follow-up studies. (c) 2007 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.