Assessment of burn severity is critical for wound treatment. Spatial frequency domain imaging (SFDI) has been previously used to characterize burns based on the relationships between histology and tissue optical properties. Recently, multispectral and hyperspectral imaging optical features have been combined with machine learning to classify burn severity. Here, we investigated the use of SFDI reflectance data at multiple wavelengths and spatial frequencies, with a support vector machine (SVM), to predict severity in a porcine model of graded burns. Burn severity predictions using SVM were compared to burn grade determined using histology techniques. Results suggest that the combination of spatial frequency data with machine learning models has the potential for accurately predicting burn severity at the 24 hr postburn time point.

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Rebecca Rowland, Adrien Ponticorvo, Melissa Baldado, Gordon T. Kennedy, David M. Burmeister, Robert J. Christy, Nicole P. Bernal, and Anthony J. Durkin, "A simple burn wound severity assessment classifier based on spatial frequency domain imaging (SFDI) and machine learning," Proc. SPIE 10851, Photonics in Dermatology and Plastic Surgery 2019, 1085109 (Presented at SPIE BiOS: February 02, 2019; Published: 26 February 2019); https://doi.org/10.1117/12.2510670.