{"paper_id":"31e7a543-0e5c-4fe2-ade6-7cc3f0a87c34","body_text":"Terahertz Spectral Imaging for the Assessment of Frostbite Injuries Using the Double Debye Model and Supervised Machine Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Terahertz Spectral Imaging for the Assessment of Frostbite Injuries Using the Double Debye Model and Supervised Machine Learning Erica Heller, Zachery B. Harris, Arash Karimi, Shi Fu, Huiting Luo, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8920090/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Early assessment of the severity of frostbite injuries is critical for guiding clinical management and improving patient outcomes; however, tissue damage evolves dynamically and is difficult to predict during the acute phase. Terahertz time-domain spectroscopy (THz-TDS) has previously demonstrated high accuracy in the early triage of thermal burn injuries. In this study, we evaluate the potential of the THz-TDS modality using a portable handheld scanner to assess the depth of frostbite wounds. A standardized \\textit{in vivo} porcine frostbite model was employed, and injury classification was performed using a support vector machine algorithm. We demonstrate that the area under the receiver operating characteristics (ROC) curves was 0.94, 0.85, and 0.87 for healthy tissue, shallow frostbite, and deep injuries. In addition, we explored the use of the double Debye dielectric relaxation model of tissue to reduce the data dimensionality. We observed significant statistical differences between the double Debye parameters of the three groups. These results demonstrate the potential of THz-TDS imaging for early, non-destructive assessment of the depth of frostbite injuries and suggest its potential utility in improving clinical decision-making and surgical outcomes. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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