The accuracy of current burn triage techniques has remained between 50-70%. Accordingly, there is a significant clinical need for the quantitative and accurate assessment of partial-thickness burn injuries. Porcine skin represents the closest animal model to human skin, and is often used in surgical skin grafting procedures. In this study, we used a standardized in vivo porcine burn model to obtain terahertz (THz) point-spectroscopy measurements from burns with various severities. We then extracted two reflection hyperspectral parameters, namely spectral area under the curve between approximately 0.1 and 0.9 THz (−10 dB bandwidth in each spectrum), and spectral slope, to characterize each burn. Using a linear combination of these two parameters, we accurately classified deep partial- and superficial partial-thickness burns (p = 0.0159), compared to vimentin immunohistochemistry as the gold standard for burn depth determination.

We present an automatic classification strategy for early and accurate assessment of burn injuries using terahertz (THz) time-domain spectroscopic imaging. Burn injuries of different severity grades, representing superficial partial-thickness (SPT), deep partial-thickness (DPT), and full-thickness (FT) wounds, were created by a standardized porcine scald model. THz spectroscopic imaging was performed using our new fiber-coupled Portable HAndheld Spectral Reflection Scanner, incorporating a telecentric beam steering configuration and an f-$$\theta$$ θ scanning lens. ASynchronous Optical Sampling in a dual-fiber-laser THz spectrometer with 100 MHz repetition rate enabled high-speed spectroscopic measurements. Given twenty-four different samples composed of ten scald and ten contact burns and four healthy samples, supervised machine learning algorithms using THz-TDS spectra achieved areas under the receiver operating characteristic curves of 0.88, 0.93, and 0.93 when differentiating between SPT, DPT, and FT burns, respectively, as determined by independent histological assessments. These results show the potential utility of our new broadband THz PHASR Scanner for early and accurate triage of burn injuries.

Terahertz (THz) imaging is a widely used technique in the study and detection of many chemicals and biomolecules in polycrystalline form because the spectral absorption signatures of these target materials often lie in the THz frequencies. When the size of dielectric grain boundaries are comparable to the THz wavelengths, spectral features can be obscured due to electromagnetic scattering. In this study, we first investigate this granular scattering effect in identification of chemicals with THz spectral absorption features. We then will propose a signal processing technique in the so-called “quefrency” domain to improve the ability to resolve these spectral features in the diffuse scattered THz images. We created a pellet with α -lactose monohydrate and riboflavin, two biologically significant materials with well-known vibrational spectral resonances, and buried the pellet in a highly scattering medium. THz transmission measurements were taken at all angles covering the half focal plane. We show that, while spectral features of lactose and riboflavin cannot be distinguished in the scattered image, application of cepstrum filtering can mitigate these scattering effects. By employing our quefrency-domain signal processing technique, we were able to unambiguously detect the dielectric resonance of lactose in the diffused scattering geometries. Finally we will discuss the limitation of the new proposed technique in spectral identification of chemicals.

The accuracy of clinical assessment techniques in diagnosing partial‐thickness burn injuries has remained as low as 50–76%. Depending on the burn depth and environmental factors in the wound, such as reactive oxygen species, inflammation, and autophagy, partial‐thickness burns can heal spontaneously or require surgical intervention. Herein, it is demonstrated that terahertz time‐domain spectral imaging (THz‐TDSI) is a promising tool for in vivo quantitative assessment and monitoring of partial‐thickness burn injuries in large animals. We used a novel handheld THz‐TDSI scanner to characterize burn injuries in a porcine scald model with histopathological controls. Statistical analysis (n = 40) indicates that the THz‐TDSI modality can accurately differentiate between partial‐thickness and full‐thickness burn injuries (1‐way ANOVA, p < 0.05). THz‐TDSI has the potential to improve burn care outcomes by helping surgeons in making objective decisions for early excision of the wound.

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.