ArticleBiomedical optics express2022
Deep neural network classification of in vivo burn injuries with different etiologies using terahertz time-domain spectral imaging.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Terahertz spectral imaging for early assessment of frostbite injuries using the double Debye model and supervised machine learning.Biomedical optics express · 2026Article
- Terahertz Mie scattering in tissue: diffuse polarimetric imaging and Monte Carlo validation in highly attenuating media models.Journal of biomedical optics · 2025Article
- THz Polarimetric Imaging of Carbon Fiber-Reinforced Composites Using the Portable Handled Spectral Reflection (PHASR) Scanner.Sensors (Basel, Switzerland) · 2024Article
- A handheld polarimetric imaging device and calibration technique for accurate mapping of terahertz Stokes vectors.Scientific reports · 2024Article
- Review of machine learning for optical imaging of burn wound severity assessment.Journal of biomedical optics · 2024Review
- Triage ofBiomedical optics express · 2023Article
- Accurate and early prediction of the wound healing outcome of burn injuries using the wavelet Shannon entropy of terahertz time-domain waveforms.Journal of biomedical optics · 2022Article
- Terahertz PHASR Scanner with 2 kHz, 100 picosecond Time-Domain Trace Acquisition Rate and an Extended Field-of-View Based on a Heliostat Design.IEEE transactions on terahertz science and technology · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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Registered trials
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