Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
MD Program, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Orthop Surg. 2022 Sep;14(9):2276-2285. doi: 10.1111/os.13406. Epub 2022 Aug 1.
One of the major difficulties in spinal surgery is the injury of important tissues caused by tissue misclassification, which is the source of surgical complications. Accurate recognization of the tissues is the key to increase safety and effect as well as to reduce the complications of spinal surgery. The study aimed at tissue recognition in the spinal operation area based on electrical impedance and the boundaries of electrical impedance between cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus.
Two female white swines with body weight of 40 kg were used to expose cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus under general anesthesia and aseptic conditions. The electrical impedance of these tissues at 12 frequencies (in the range of 10-100 kHz) was measured by electrochemical analyzer with a specially designed probe, at 22.0-25.0°C and 50%-60% humidity. Two types of tissue recognition models - one combines principal component analysis (PCA) and support vector machine (SVM) and the other combines combines SVM and ensemble learning - were constructed, and the boundaries of electrical impedance of the five tissues at 12 frequencies of current were figured out. Linear correlation, two-way ANOVA, and paired T-test were conducted to analyze the relationship between the electrical impedance of different tissues at different frequencies.
The results suggest that the differences of electrical impedance mainly came from tissue type (p < 0.0001), the electrical impedance of five kinds of tissue was statistically different from each other (p < 0.0001). The tissue recognition accuracy of the algorithm based on principal component analysis and support vector machine ranged from 83%-100%, and the overall accuracy was 95.83%. The classification accuracy of the algorithm based on support vector machine and ensemble learning was 100%, and the boundaries of electrical impedance of five tissues at various frequencies were calculated.
The electrical impedance of cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus had significant differences in 10-100 kHz frequency. The application of support vector machine realized the accurate tissue recognition in the spinal operation area based on electrical impedance, which is expected to be translated and applied to tissue recognition during spinal surgery.
在脊柱手术中，一个主要的困难是由于组织分类错误导致的重要组织损伤，这是手术并发症的根源。准确识别组织是提高安全性和效果、降低脊柱手术并发症的关键。本研究旨在基于电导率和皮质骨、松质骨、脊髓、肌肉和髓核之间的电导率边界，实现脊柱手术区域的组织识别。
在全麻和无菌条件下，使用两只体重为 40 公斤的雌性白猪暴露皮质骨、松质骨、脊髓、肌肉和髓核。使用电化学分析仪和专门设计的探头，在 22.0-25.0°C 和 50%-60%湿度下，测量这几种组织在 12 个频率（10-100 kHz 范围内）的电导率。构建了两种组织识别模型——一种是结合主成分分析（PCA）和支持向量机（SVM）的模型，另一种是结合 SVM 和集成学习的模型，并得出了这五种组织在 12 个电流频率下的电导率边界。采用线性相关、双向方差分析和配对 T 检验分析不同频率下不同组织的电导率之间的关系。
结果表明，电导率的差异主要来自组织类型（p<0.0001），五种组织的电导率彼此之间存在统计学差异（p<0.0001）。基于主成分分析和支持向量机的算法的组织识别准确率在 83%-100%之间，总体准确率为 95.83%。基于支持向量机和集成学习的算法的分类准确率为 100%，并计算了五种组织在不同频率下的电导率边界。
皮质骨、松质骨、脊髓、肌肉和髓核在 10-100 kHz 频率范围内的电导率存在显著差异。支持向量机的应用实现了基于电导率的脊柱手术区域的准确组织识别，有望被翻译并应用于脊柱手术中的组织识别。
