Neural network based algorithm to quantify joint space width in joints of the hand for arthritis assessment.

Arthritis diseases are widespread with enormous societal costs. The two most common forms, rheumatoid arthritis and osteoarthritis, affect joints of the hand and cause narrowing of the joint spaces as the disease destroys the articular cartilage. Radiographic assessment is one of the most promising tools to detect subtle changes in joint space width (JSW), and therefore disease progression. Currently radiographic assessment of arthritis in joints of the hand is accomplished though semiquantitative subjective scoring systems which do not provide a quantitative measurement of the JSW. We describe here an automated method which calculates the average JSW of the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joint spaces for fingers 2 to 5 (index, middle, ring, and little) on digitized hand radiographs. The method was tested with a set of 54 hand radiographs on joints with mild to moderate rheumatoid arthritis. Performance was evaluated by comparing algorithm measured JSW to a gold standard determined from expertly hand-drawn joint margins. The agreement was quantified by a measurement of root mean square deviation, 0.148 mm, 0.089 mm, and 0.114 mm for the MCP, PIP, and DIP joints, respectively. In addition, the algorithm measured JSW strongly correlated with the gold standard: R2=0.80 (MCP), R2= 0.82 (PIP), and R2= 0.84 (DIP). This is an accurate and robust algorithm and should provide a more quantitative measure of disease progression than current methods.