Open AccessDeep neural network classification of in vivo burn injuries with different etiologies using terahertz time-domain spectral imaging
Author(s) -
Omar B. Osman,
Zachery B. Harris,
Mahmoud E. Khani,
Juin W. Zhou,
Andrew Chen,
Adam J. Singer,
M. Hassan Arbab
Publication year - 2022
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.452257
Subject(s) - terahertz radiation , hyperspectral imaging , artificial intelligence , terahertz spectroscopy and technology , computer science , scanner , deep learning , artificial neural network , pattern recognition (psychology) , optics , materials science , biomedical engineering , medicine , optoelectronics , physics
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.