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用于烧伤深度诊断的双成像系统。

Dual-imaging system for burn depth diagnosis.

作者信息

Ganapathy Priya, Tamminedi Tejaswi, Qin Yi, Nanney Lillian, Cardwell Nancy, Pollins Alonda, Sexton Kevin, Yadegar Jacob

机构信息

UtopiaCompression Corporation, 11150W Olympic Blvd., Suite # 320, Los Angeles, CA 90064, USA.

UtopiaCompression Corporation, 11150W Olympic Blvd., Suite # 320, Los Angeles, CA 90064, USA.

出版信息

Burns. 2014 Feb;40(1):67-81. doi: 10.1016/j.burns.2013.05.004. Epub 2013 Jun 18.

DOI:10.1016/j.burns.2013.05.004
PMID:23790396
Abstract

Currently, determination of burn depth and healing outcomes has been limited to subjective assessment or a single modality, e.g., laser Doppler imaging. Such measures have proven less than ideal. Recent developments in other non-contact technologies such as optical coherence tomography (OCT) and pulse speckle imaging (PSI) offer the promise that an intelligent fusion of information across these modalities can improve visualization of burn regions thereby increasing the sensitivity of the diagnosis. In this work, we combined OCT and PSI images to classify the degree of burn (superficial, partial-thickness and full-thickness burns). Algorithms were developed to integrate and visualize skin structure (with and without burns) from the two modalities. We have completed the proposed initiatives by employing a porcine burn model and compiled results that attest to the utility of our proposed dual-modal fusion approach. Computer-derived data indicating the varying burn depths were validated through immunohistochemical analysis performed on burned skin tissue. The combined performance of OCT and PSI modalities provided an overall ROC-AUC=0.87 (significant at p<0.001) in classifying different burn types measured after 1-h of creating the burn wounds. Porcine model studies to assess feasibility of this dual-imaging system for wound tracking are underway.

摘要

目前,烧伤深度和愈合结果的判定仅限于主观评估或单一模式,例如激光多普勒成像。事实证明,这些措施并不理想。其他非接触技术的最新进展,如光学相干断层扫描(OCT)和脉冲散斑成像(PSI),有望通过跨这些模式的智能信息融合来改善烧伤区域的可视化,从而提高诊断的敏感性。在这项工作中,我们将OCT和PSI图像相结合,以对烧伤程度(浅度、深度和全层烧伤)进行分类。开发了算法,以整合和可视化来自这两种模式的皮肤结构(有烧伤和无烧伤)。我们通过使用猪烧伤模型完成了所提出的计划,并汇总了结果,证明了我们提出的双模式融合方法的实用性。通过对烧伤皮肤组织进行免疫组织化学分析,验证了计算机得出的表明不同烧伤深度的数据。在烧伤伤口形成1小时后测量不同烧伤类型时,OCT和PSI模式的综合性能在分类中提供了总体ROC-AUC = 0.87(p<0.001时具有显著性)。评估这种双成像系统用于伤口跟踪可行性的猪模型研究正在进行中。

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