Validation of automatic joint space width measurements in hand radiographs in rheumatoid arthritis

Abstract

Abstract. Computerized methods promise quick, objective, and sensitive tools to quantify progression of radiological damage in rheumatoid arthritis (RA). Measurement of joint space width (JSW) in finger and wrist joints with these systems performed comparable to the Sharp-van der Heijde score (SHS). A next step toward clinical use, validation of precision and accuracy in hand joints with minimal damage, is described with a close scrutiny of sources of error. A recently developed system to measure metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints was validated in consecutive hand images of RA patients. To assess the impact of image acquisition, measurements on radiographs from a multicenter trial and from a recent prospective cohort in a single hospital were compared. Precision of the system was tested by comparing the joint space in mm in pairs of subsequent images with a short interval without progression of SHS. In case of incorrect measurements, the source of error was analyzed with a review by human experts. Accuracy was assessed by comparison with reported measurements with other systems. In the two series of radiographs, the system could automatically locate and measure 1003/1088 (92.2%) and 1143/1200 (95.3%) individual joints, respectively. In joints with a normal SHS, the average (SD) size of MCP joints was 1.7 AE 0.2 and 1.6 AE 0.3 mm in the two series of radiographs, and of PIP joints 1.0 AE 0.2 and 0.9 AE 0.2 mm. The difference in JSW between two serial radiographs with an interval of 6 to 12 months and unchanged SHS was 0.0 AE 0.1 mm, indicating very good precision. Errors occurred more often in radiographs from the multicenter cohort than in a more recent series from a single hospital. Detailed analysis of the 55/1125 (4.9%) measurements that had a discrepant paired measurement revealed that variation in the process of image acquisition (exposure in 15% and repositioning in 57%) was a more frequent source of error than incorrect delineation by the software (25%). Various steps in the validation of an automated measurement system for JSW of MCP and PIP joints are described. The use of serial radiographs from different sources, with a short interval and limited damage, is helpful to detect sources of error. Image acquisition, in particular repositioning, is a dominant source of error. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Modelling rheumatoid arthritis: A hybrid modelling framework to describe pannus formation in a small joint

“…The averages between the age groups were all roughly the same, (1.1mm, 1.06mm, 1.02mm, respectively). Similarly, in [88] the authors use radiographs to validate the joint space width (JSW) calculations in undamaged MCP and PIP joints of patients with early rheumatoid arthritis (RA). Two dutch data sets were used to validate the method, where radiographs were taken every 6 months of the patients.…”

“…The main symptoms of RA include inflammation, pain, swelling and stiffness of joints, fatigue and weight loss. Generally, the smaller joints in the hands and feet are most likely to be affected [75], for example, proximal interphalangeal (PIP) joints, which are the middle joints on the non-thumb fingers of the hand, are most commonly affected [33, 34, 86, 88]. A key aspect of RA progression within a joint is the formation of a ‘pannus’ from the abnormally growing synovial membrane.…”

Fully automated joint space width measurement and digital X-ray radiogrammetry in early RA

“…5 In order to minimise the aforementioned limitations, semiautomated and fully automated software to better evaluate joint space width (JSW) have been developed and reviewed. [6][7][8][9][10] The benefits of a fully automated program include higher reproducibility, faster results, observer independence and a higher sensitivity. [11][12][13] Moreover, bone mineral density (BMD) measured with digital X-ray radiogrammetry (DXR) is a recent tool that quantifies BMD in metacarpals 2, 3 and 4.…”

Active Latent Space Shape Model: A Bayesian Treatment of Shape Model Adaptation with an Application to Psoriatic Arthritis Radiographs

“…Hence, models that explicitly combine shape and appearance have had more success at detecting geometric shape features than their deep learning counterparts had at detecting texture based features [10,11,28]. More recently, clinical studies have been performed to assess methods relying on shape and appearance [13,21].…”