Classification of Burn Injury using Raman Spectroscopy and Optical Coherence Tomography: An Ex-Vivo Study on Porcine Skin

Accurate depth assessment of burn wounds is a critical task to provide the right treatment and care. Currently, laser Doppler imaging is able to provide better accuracy compared to the standard clinical evaluation. However, its clinical applicability is limited by factors like scanning distance, time, and cost. Precise diagnosis of burns requires adequate structural and functional details. In this work, we evaluated the combined potential of two non-invasive optical modalities, optical coherence tomography (OCT) and Raman spectroscopy (RS), to identify degrees of burn wounds (superficial partial-thickness (SPT), deep partial-thickness (DPT), and full-thickness (FT)). OCT provides morphological information, whereas, RS provides biochemical aspects. OCT images and Raman spectra were obtained from burns created on ex-vivo porcine skin. Algorithms were developed to segment skin region and extract textural features from OCT images, and derive spectral wave features from RS. These computed features were fed into machine learning classifiers for categorization of burns. Histological results obtained from trichrome staining were used as ground-truth. The combined performance of RS-OCT reported an overall average accuracy of 85% and ROC-AUC = 0.94, in distinguishing the burn wounds. The significant performance on ex vivo skin motivates to assess the feasibility of combined RS-OCT in in vivo models..

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