1. Skin Surface Temperatures Measured by Thermal Imaging Aid in the Diagnosis of Cellulitis 
Lauren N. Ko, Adam B. Raff, Anna C. Garza-Mayers, Allison S. Dobry, Antonio Ortega-Martinez, R. Rox Anderson, Daniela Kroshinsky 
Journal of Investigative Dermatology 
Volume 138, Issue 3, Pages (March 2018)
DOI: /j.jid
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2. Figure 1
Screening and enrollment of the study. This flowchart shows study screening and enrollment. Abx, antibiotics; Post-op, postoperative.
Journal of Investigative Dermatology  , DOI: ( /j.jid )
Copyright © 2017 The Authors Terms and Conditions

3. Figure 2
Receiver operating characteristic curve. Receiver operating characteristic curve for predicting cellulitis in patients randomized to dermatology consultation (n = 40). AUC, area under the curve.
Journal of Investigative Dermatology  , DOI: ( /j.jid )
Copyright © 2017 The Authors Terms and Conditions

4. Figure 3
Model 1: predicting cellulitis based on temperature differences. Predictive model. Using logistic regression, we determined the probability of cellulitis based on temperature difference between affected and unaffected skin. Red circles indicate cellulitis patients, and green circles indicate pseudocellulitis patients; 50% threshold corresponds to 0.47°C.
Journal of Investigative Dermatology  , DOI: ( /j.jid )
Copyright © 2017 The Authors Terms and Conditions