Yin Meifang, Li Yongming, Luo Yongquan, Yuan Mingzhou, Armato Ubaldo, Prà Ilaria Dal, Zhang Lijun, Zhang Dayong, Wei Yating, Yang Guang, Huang Lixian, Wang Pin, Wu Jun
Department of Burn and Plastic Surgery, Institute Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen 518035, China.
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Burns Trauma. 2021 Jul 9;9:tkab014. doi: 10.1093/burnst/tkab014. eCollection 2021.
The accurate and objective evaluation of burn depth is a significant challenge in burn wound care. Herein, we used near infrared spectroscopy (NIRS) technology to measure the different depth of thermal burns in porcine models. Based on the intensity of the spectral signals and the diffuse reflection theory, we extracted the optical parameters involved in functional (total hemoglobin and water content) and structural (tissue scattered size and scattered particles) features that reflect the changes in burn depth. Next, we applied support vector regression to construct a model including the optical property parameters and the burn depth. Finally, we histologically verified the burn depth data collected via NIRS. The results showed that our inversion model could achieve an average relative error of about 7.63%, while the NIRS technology diagnostic accuracy was in the range of 50 μm. For the first time, this novel technique provides physicians with real-time burn depth information objectively and accurately.
烧伤深度的准确客观评估是烧伤创面护理中的一项重大挑战。在此,我们使用近红外光谱(NIRS)技术测量猪模型中不同深度的热烧伤。基于光谱信号强度和漫反射理论,我们提取了反映烧伤深度变化的功能(总血红蛋白和含水量)和结构(组织散射大小和散射颗粒)特征所涉及的光学参数。接下来,我们应用支持向量回归构建一个包含光学性质参数和烧伤深度的模型。最后,我们通过组织学方法验证了通过NIRS收集的烧伤深度数据。结果表明,我们的反演模型平均相对误差约为7.63%,而NIRS技术的诊断精度在50μm范围内。这项新技术首次为医生提供了客观准确的实时烧伤深度信息。