Heredia-Juesas Juan, Thatcher Jeffrey E, Lu Yang, Squiers John J, King Darlene, Fan Wensheng, DiMaio J Michael, Martinez-Lorenzo Jose A
Departments of Electrical & Computer and Mechanical & Industrial Engineering, Northeastern University, Boston, MA, USA.
Spectral MD, Inc. Dallas, TX, USA.
Biomed Opt Express. 2018 Mar 22;9(4):1809-1826. doi: 10.1364/BOE.9.001809. eCollection 2018 Apr 1.
The process of burn debridement is a challenging technique requiring significant skills to identify the regions that need excision and their appropriate excision depths. In order to assist surgeons, a machine learning tool is being developed to provide a quantitative assessment of burn-injured tissue. This paper presents three non-invasive optical imaging techniques capable of distinguishing four kinds of tissue-healthy skin, viable wound bed, shallow burn, and deep burn-during serial burn debridement in a porcine model. All combinations of these three techniques have been studied through a k-fold cross-validation method. In terms of global performance, the combination of all three techniques significantly improves the classification accuracy with respect to just one technique, from 0.42 up to more than 0.76. Furthermore, a non-linear spatial filtering based on the mode of a small neighborhood has been applied as a post-processing technique, in order to improve the performance of the classification. Using this technique, the global accuracy reaches a value close to 0.78 and, for some particular tissues and combination of techniques, the accuracy improves by 13%.
烧伤清创过程是一项具有挑战性的技术，需要高超的技能来识别需要切除的区域及其合适的切除深度。为了协助外科医生，正在开发一种机器学习工具，以对烧伤受损组织进行定量评估。本文介绍了三种非侵入性光学成像技术，这些技术能够在猪模型的连续烧伤清创过程中区分四种组织——健康皮肤、有活力的创面床、浅度烧伤和深度烧伤。通过k折交叉验证方法研究了这三种技术的所有组合。在整体性能方面，与仅使用一种技术相比，三种技术的组合显著提高了分类准确率，从0.42提高到了0.76以上。此外，为了提高分类性能，已应用基于小邻域模式的非线性空间滤波作为后处理技术。使用该技术，整体准确率达到接近0.78的值，对于某些特定组织和技术组合，准确率提高了13%。
