Accurate and early prediction of the wound healing outcome of burn injuries using the wavelet Shannon entropy of THz time-domain waveforms

Abstract Significance: Severe burn injuries cause significant hypermetabolic alterations that are highly dynamic, hard to predict, and require acute and critical care. The clinical assessments of the severity of burn injuries are highly subjective and have been reported to be inaccurate consistently. Therefore, the utilization of other imaging modalities is crucial to reach an objective and accurate burn assessment modality. Aim: We describe a non-invasive technique using terahertz time-domain spectroscopy (THz-TDS) and the wavelet packet Shannon entropy to automatically estimate the burn depth and predict the wound healing outcome of the thermal burn injuries. Approach: We created forty burn injuries of different severity grades in two porcine models using scald and contact methods of infliction. We used our THz Portable Handheld Spectral Reflection (PHASR) Scanner to obtain the in vivo THz-TDS images. We used the energy to Shannon entropy ratio of the wavelet packet coefficients of the THz-TDS waveforms on Day 0 to create supervised support vector machine (SVM) classification models. Histological20 assessments of the burn biopsies serve as the ground truth. Results: We achieved an accuracy rate of 94.7% in predicting the wound healing outcome, as determined by histological measurement of the re-epithelialization rate on Day 28 post-burn induction, using the THz-TDS measurements obtained on Day 0. Furthermore, we report the accuracy rates of 89%, 87.1%, and 87.6% in automatic diagnosis of the superficial partial-thickness, deep partial-thickness, and full-thickness burns, respectively, using a multiclass SVM model. Conclusions: The THz PHASR Scanner promises a robust, high-speed, and accurate diagnostic modality to improve the clinical triage of burns and their management.