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.

Date Published: 26 February 2019
PDF: 11 pages
Proc. SPIE 10851, Photonics in Dermatology and Plastic Surgery 2019, 1085109 (26 February 2019); doi: 10.1117/12.2510670

Published in SPIE Proceedings Vol. 10851:
Photonics in Dermatology and Plastic Surgery 2019
Bernard Choi; Haishan Zeng, Editor(s)