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 520-526 (March 2018) DOI: 10.1016/j.jid.2017.09.022 Copyright © 2017 The Authors Terms and Conditions

Figure 1 Screening and enrollment of the study. This flowchart shows study screening and enrollment. Abx, antibiotics; Post-op, postoperative. Journal of Investigative Dermatology 2018 138, 520-526DOI: (10.1016/j.jid.2017.09.022) Copyright © 2017 The Authors Terms and Conditions

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 2018 138, 520-526DOI: (10.1016/j.jid.2017.09.022) Copyright © 2017 The Authors Terms and Conditions

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 2018 138, 520-526DOI: (10.1016/j.jid.2017.09.022) Copyright © 2017 The Authors Terms and Conditions