Ann Rheum Dis 64:239-245 doi:10.1136/ard.2004.024224
  • Extended report

First clinical evaluation of sagittal laser optical tomography for detection of synovitis in arthritic finger joints

  1. A K Scheel1,
  2. M Backhaus2,
  3. A D Klose3,
  4. B Moa-Anderson3,
  5. U J Netz4,
  6. K-G A Hermann5,
  7. J Beuthan4,
  8. G A Müller1,
  9. G R Burmester2,
  10. A H Hielscher3
  1. 1Department of Medicine, Nephrology and Rheumatology, Georg-August-University Göttingen, Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
  2. 2Department of Rheumatology and Clinical Immunology, Charité-University Medicine Berlin, Schumannstrasse 20/21, D-10098 Berlin, Germany
  3. 3Departments of Biomedical Engineering and Radiology, Columbia University, ET351 Mudd Building, MC 8904, 500 West 120th Street, New York, NY 10027, USA
  4. 4Department of Medical Physics and Laser Medicine, Free University of Berlin, Fabeckstrasse 60-62, D-14195 Berlin, Germany
  5. 5Department of Radiology, Charité-University Medicine Berlin, Schumannstrasse 20/21, D-10098 Berlin, Germany
  1. Correspondence to:
    Dr A K Scheel
    Department of Medicine, Nephrology and Rheumatology, Robert-Koch-Strasse 40, D-37075 Göttingen, Germany;
  • Accepted 16 July 2004
  • Published Online First 5 August 2004


Objective: To identify classifiers in images obtained with sagittal laser optical tomography (SLOT) that can be used to distinguish between joints affected and not affected by synovitis.

Methods: 78 SLOT images of proximal interphalangeal joints II–IV from 13 patients with rheumatoid arthritis were compared with ultrasound (US) images and clinical examination (CE). SLOT images showing the spatial distribution of scattering and absorption coefficients within the joint cavity were generated. The means and standard errors for seven different classifiers (operator score and six quantitative measurements) were determined from SLOT images using CE and US as diagnostic references. For classifiers showing significant differences between affected and non-affected joints, sensitivities and specificities for various cut off parameters were obtained by receiver operating characteristic (ROC) analysis.

Results: For five classifiers used to characterise SLOT images the mean between affected and unaffected joints was statistically significant using US as diagnostic reference, but statistically significant for only one classifier with CE as reference. In general, high absorption and scattering coefficients in and around the joint cavity are indicative of synovitis. ROC analysis showed that the minimal absorption classifier yields the largest area under the curve (0.777; sensitivity and specificity 0.705 each) with US as diagnostic reference.

Conclusion: Classifiers in SLOT images have been identified that show statistically significant differences between joints with and without synovitis. It is possible to classify a joint as inflamed with SLOT, without the need for a reference measurement. Furthermore, SLOT based diagnosis of synovitis agrees better with US diagnosis than CE.