  A computer-aided interpretation approach is proposed to detect rheumatic
arthritis (RA) of human finger joints in optical tomographic images. The image
interpretation method employs a multi-variate signal detection analysis aided
by a machine learning classification algorithm, called Self-Organizing Mapping
(SOM). Unlike in previous studies, this allows for combining multiple physical
image parameters, such as minimum and maximum values of the absorption
coefficient for identifying affected and not affected joints. Classification
performances obtained by the proposed method were evaluated in terms of
sensitivity, specificity, Youden index, and mutual information. Different
methods (i.e., clinical diagnostics, ultrasound imaging, magnet resonance
imaging and inspection of optical tomographic images), were used as "ground
truth"-benchmarks to determine the performance of image interpretations. Using
data from 100 finger joints, findings suggest that some parameter combinations
lead to higher sensitivities while others to higher specificities when compared
to single parameter classifications employed in previous studies. Maximum
performances were reached when combining minimum/maximum-ratio and image
variance with respect to ultra sound as benchmark. In this case, sensitivity
and specificity of 0.94 and 0.96 respectively were achieved. These values are
much higher than results reported when a) other classification techniques were
applied or b) single parameter classifications were used, where sensitivities
and specificities of 0.71 were achieved.
