A computer-aided interpretation approach is proposed to detect rheumatic arthritis (RA) in human finger joints using optical tomographic images. The image interpretation method employs a classification algorithm that makes use of a so-called self-organizing mapping scheme to classify fingers as either affected or unaffected by RA. Unlike in previous studies, this allows for combining multiple image features, 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 to produce 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 are reached when combining the minimum/maximum ratio of the absorption coefficient and image variance. In this case, sensitivities and specificities over 0.9 can be achieved. These values are much higher than values obtained when only single parameter classifications were used, where sensitivities and specificities remained well below 0.8.
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).
A recent research study has shown that combining multiple parameters, drawn from optical tomographic images,
leads to better classification results to identifying human finger joints that are affected or not affected
by rheumatic arthritis RA. Building up on the research findings of the previous study, this article presents an
advanced computer-aided classification approach for interpreting optical image data to detect RA in finger joints.
Additional data are used including, for example, maximum and minimum values of the absorption coefficient
as well as their ratios and image variances. Classification performances obtained by the proposed method were
evaluated in terms of sensitivity, specificity, Youden index and area under the curve AUC. Results were compared
to different benchmarks ("gold standard"): magnet resonance, ultrasound and clinical evaluation. Maximum accuracies
(AUC=0.88) were reached when combining minimum/maximum-ratios and image variances and using
ultrasound as gold standard.
This research study explores the combined use of more than one parameter derived from optical tomographic images to increase diagnostic accuracy which is measured in terms of sensitivity and specificity. Parameters considered include, for example, smallest or largest absorption or scattering coefficients or the ratios thereof in an image region of interest. These parameters have been used individually in a previous study to determine if a finger joint is affected or not affected by rheumatoid arthritis. To combine these parameters in the analysis we employ here a vector quantization based classification method called Self-Organizing Mapping (SOM). This method allows producing multivariate ROC-curves from which sensitivity and specificities can be determined. We found that some parameter combinations can lead to higher sensitivities whereas others to higher specificities when compared to singleparameter classifications employed in previous studies. The best diagnostic accuracy, in terms of highest Youden index, was achieved by combining three absorption parameters [maximum(µa), minimum(µa), and the ratio of minimum(µa) and maximum(µa)], which result in a sensitivity of 0.78, a specificity of 0.76, a Youden index of 0.54, and an area under the curve (AUC) of 0.72. These values are higher than for previously reported single parameter classifications with a best sensitivity and specificity of 0.71, a Youden index of 0.41, and an AUC of 0.66.
We found that using more than one parameter derived from optical tomographic images can lead to better image classification results compared to cases when only one parameter is used.. In particular we present a multi-parameter classification approach, called self-organizing mapping (SOM), for detecting synovitis in arthritic finger joints based on sagittal laser optical tomography (SLOT). This imaging modality can be used to determine various physical parameters such as minimal absorption and scattering coefficients in an image of the proximal interphalengeal joint. Results were compared to different gold standards: magnet resonance imaging, ultra-sonography and clinical evaluation. When compared to classifications based on single-parameters, e.g., absorption minimum only, the study reveals that multi-parameter classifications lead to higher classification sensitivities and specificities and statistical significances with p-values <5 per cent. Finally, the data suggest that image analyses are more reliable and avoid ambiguous interpretations when using more than one parameter.