利用机器学习和多光谱空间频域成像数据来表征毫米波源的烧伤

Biophotonics Congress: Optics in the Life Sciences 2023 (OMA, NTM, BODA, OMP, BRAIN) Pub Date : 1900-01-01 DOI:10.1364/omp.2023.oth1e.7

R. H. Wilson, G. Kennedy, C. Campbell, W. Voorhees, James E. Parker, C. Kowalczewski, Jason Payne, A. Kowalczewski, R. Christy, Jeffrey Whitmore, R. Stone, Anthony J. Durkin

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摘要

准确、及时的烧伤严重程度分类对患者护理至关重要。我们使用非接触式多光谱空间频域成像技术结合机器学习算法对临床前模型中的毫米波辐射烧伤进行分类。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Using Machine Learning with Multispectral Spatial Frequency Domain Imaging Data to Characterize Burns from Millimeter-Wave Sources

Accurate, prompt classification of burn wound severity is crucial for patient care. We use non-contact multispectral spatial frequency domain imaging technology coupled with machine learning algorithms to categorize millimeter-wave radiation burns in a preclinical model.

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Biophotonics Congress: Optics in the Life Sciences 2023 (OMA, NTM, BODA, OMP, BRAIN)

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