Optical imaging for diagnosis of rheumatoid arthritis automatic versus human evaluation

Successful detection of rheumatoid arthritis (RA) at the early stages of development can significantly enhance the chances of effective therapy. The early onset of RA is often marked with inflammation of the synovial lining of the joint, a condition known as synovitis. Effective imaging of synovitis is therefore of critical importance. While dynamic, contrast-enhanced magnetic resonance imaging (MRI) is capable of effective imaging of synovitis, it is a costly modality. As an alternative, inexpensive approach, optical imaging post injection of the near-infrared fluorescent dye indocynine green (ICG) has been recently proposed for imaging RA. Evaluation of the obtained optical images is performed via examination by trained human readers. However, optical imaging has yet to achieve the diagnostic accuracy of MRI. In this paper we present a method for automatic evaluation of the fluorescence images and compare its performance with the human-based evaluation. Our method relies on our previous work on spatiotemporal analysis of image sequence with principal component analysis (PCA) to seek synovitis signal components with the help of a segmentation method. The results for a group of 600 joints, obtained from 20 patients, suggest improved diagnostic performance using the automatic approach in comparison to human-based evaluation.