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Deep neural network classification of in vivo burn injuries with different etiologies using terahertz time-domain spectral imaging

Authors :
Omar B. Osman
Zachery B. Harris
Mahmoud E. Khani
Juin W. Zhou
Andrew Chen
Adam J. Singer
M. Hassan Arbab
Source :
Biomed Opt Express
Publication Year :
2022

Abstract

Thermal injuries can occur due to direct exposure to hot objects or liquids, flames, electricity, solar energy and several other sources. If the resulting injury is a deep partial thickness burn, the accuracy of a physician’s clinical assessment is as low as 50-76% in determining the healing outcome. In this study, we show that the Terahertz Portable Handheld Spectral Reflection (THz-PHASR) Scanner combined with a deep neural network classification algorithm can accurately differentiate between partial-, deep partial-, and full-thickness burns 1-hour post injury, regardless of the etiology, scanner geometry, or THz spectroscopy sampling method (ROC-AUC = 91%, 88%, and 86%, respectively). The neural network diagnostic method simplifies the classification process by directly using the pre-processed THz spectra and removing the need for any hyperspectral feature extraction. Our results show that deep learning methods based on THz time-domain spectroscopy (THz-TDS) measurements can be used to guide clinical treatment plans based on objective and accurate classification of burn injuries.

Details

ISSN :
21567085
Volume :
13
Issue :
4
Database :
OpenAIRE
Journal :
Biomedical optics express
Accession number :
edsair.doi.dedup.....eabec3a80293732658c7e257754691ae