Patellar skin surface temperature by thermography reflects knee osteoarthritis severity.

Thumbnail Image



Journal Title

Journal ISSN

Volume Title

Repository Usage Stats


Citation Stats


BACKGROUND: Digital infrared thermal imaging is a means of measuring the heat radiated from the skin surface. Our goal was to develop and assess the reproducibility of serial infrared measurements of the knee and to assess the association of knee temperature by region of interest with radiographic severity of knee Osteoarthritis (rOA). METHODS: A total of 30 women (15 Cases with symptomatic knee OA and 15 age-matched Controls without knee pain or knee OA) participated in this study. Infrared imaging was performed with a Meditherm Med2000™ Pro infrared camera. The reproducibility of infrared imaging of the knee was evaluated through determination of intraclass correlation coefficients (ICCs) for temperature measurements from two images performed 6 months apart in Controls whose knee status was not expected to change. The average cutaneous temperature for each of five knee regions of interest was extracted using WinTes software. Knee x-rays were scored for severity of rOA based on the global Kellgren-Lawrence grading scale. RESULTS: The knee infrared thermal imaging procedure used here demonstrated long-term reproducibility with high ICCs (0.50-0.72 for the various regions of interest) in Controls. Cutaneous temperature of the patella (knee cap) yielded a significant correlation with severity of knee rOA (R = 0.594, P = 0.02). CONCLUSION: The skin temperature of the patellar region correlated with x-ray severity of knee OA. This method of infrared knee imaging is reliable and as an objective measure of a sign of inflammation, temperature, indicates an interrelationship of inflammation and structural knee rOA damage.





Published Version (Please cite this version)


Publication Info

Denoble, Anna E, Norine Hall, Carl F Pieper and Virginia B Kraus (2010). Patellar skin surface temperature by thermography reflects knee osteoarthritis severity. Clin Med Insights Arthritis Musculoskelet Disord, 3. pp. 69–75. 10.4137/CMAMD.S5916 Retrieved from

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.



Carl F. Pieper

Professor of Biostatistics & Bioinformatics

Analytic Interests.

1) Issues in the Design of Medical Experiments: I explore the use of reliability/generalizability models in experimental design. In addition to incorporation of reliability, I study powering longitudinal trials with multiple outcomes and substantial missing data using Mixed models.

2) Issues in the Analysis of Repeated Measures Designs & Longitudinal Data: Use of Hierarchical Linear Models (HLM) or Mixed Models in modeling trajectories of multiple variables over time (e.g., physical and cognitive functioning and Blood Pressure). My current work involves methodologies in simultaneous estimation of trajectories for multiple variables within and between domains, modeling co-occuring change.

Areas of Substantive interest: (1) Experimental design and analysis in gerontology and geriatrics, and psychiatry,
(2) Multivariate repeated measures designs,


Virginia Byers Kraus

Mary Bernheim Distinguished Professor of Medicine

Virginia Byers Kraus, MD, PhD, is the Mary Bernheim Distinguished Professor of Medicine, Professor of Orthopaedic Surgery, Professor of Pathology and a faculty member of the Duke Molecular Physiology Institute in the Duke University School of Medicine. She is a practicing Rheumatologist with over 30 years’ experience in translational musculoskeletal research focusing on osteoarthritis, the most common of all arthritides. She trained at Brown University (ScB 1979), Duke University (MD 1982, PhD 1993) and the Duke University School of Medicine (Residency in Internal Medicine and Fellowship in Rheumatology). Her career has focused on elucidating osteoarthritis pathogenesis and translational research into the discovery and validation of biomarkers for early osteoarthritis detection, prediction of progression, monitoring of disease status, and facilitation of therapeutic developments. She is co-PI of the Foundation for NIH Biomarkers Consortium Osteoarthritis project. Trained as a molecular biologist and a Rheumatologist, she endeavors to study disease from bedside to bench.

Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.