Computer-aided classification of rheumatoid arthritis in finger joints using frequency domain optical tomography

Novel methods that can help in the diagnosis and monitoring of joint disease are essential for efficient use of novel arthritis therapies that are currently emerging. Building on previous studies that involved continuous wave imaging systems we present here first clinical data obtained with a new frequency-domain imaging system. Three-dimensional tomographic data sets of absorption and scattering coefficients were generated for 107 fingers. The data were analyzed using ANOVA, MANOVA, Discriminant Analysis DA, and a machine-learning algorithm that is based on self-organizing mapping (SOM) for clustering data in 2-dimensional parameter spaces. Overall we found that the SOM algorithm outperforms the more traditional analysis methods in terms of correctly classifying finger joints. Using SOM, healthy and affected joints can now be separated with a sensitivity of 0.97 and specificity of 0.91. Furthermore, preliminary results suggest that if a combination of multiple image properties is used, statistical significant differences can be found between RA-affected finger joints that show different clinical features (e.g. effusion, synovitis or erosion).