Published in:
Open Access 01-12-2019 | Minimally Invasive Surgery | Research article
Tissue discrimination by bioelectrical impedance during PLL resection in anterior decompression surgery for treatment of cervical spondylotic myelopathy
Published in: Journal of Orthopaedic Surgery and Research | Issue 1/2019
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Background
The electrical properties of biological tissues differ depending on their physical properties. This study aimed to explore if bioelectrical impedance (modulus and phase) would discriminate tissues relevant to resection of the posterior longitudinal ligament (PLL) in anterior cervical decompression surgery.
Methods
PLL resection via an anterior approach was performed on the C4/5 segments in six mini-pigs. The bioelectrical impedance measurements were performed for two tissue groups (annulus fibrosus, endplate cartilage, sub-endplate cortical bone, and PLL; PLL, dura mater, spinal cord, and nerve root) using a novel probe and a precision inductance-capacitance-resistance meter. For each group, impedance was analyzed in terms of modulus and phase along a broad spectrum of frequencies (200–3000 kHz) using a nonparametric statistical analysis (Kruskal-Wallis).
Results
The analysis showed a clear difference among the tissues. The modulus and phase show the same changing trend with frequency and present lower values at higher frequencies. Among annulus fibrosus, endplate cartilage, sub-endplate cortical bone, and PLL, it was possible to discriminate each tissue at every frequency point, considering the phase (p < 0.05), while this was not always the case (i.e., annulus fibrosus vs PLL at frequency of 200 kHz, 400 kHz, and 3000 kHz, p > 0.05) for modulus. Among PLL, dura mater, spinal cord, and nerve root, for every comparison, a statistically significant difference was reported in the modulus, phase, or both (p < 0.05).
Conclusions
The results indicated the potential of bioelectrical impedance to provide real-time tissue differentiation and enhance safe PLL resection in anterior cervical decompression surgery, particularly in robot-assisted minimally invasive surgery (RMIS).