Bioimpedance assessment method based on back propagation neural network for irreversible electroporation of liver tissue.

The safety and efficacy of irreversible electroporation (IRE) in tumor therapy has been validated over many years by clinical application. An in-depth study, however, is required to assess the degree of ablation during the clinical dissemination of the treatment. In this study, we propose and validate a method to evaluate the degree of IRE by measuring the impedance spectra of tissues before and after pulsed electric field treatment. IRE with varying parameters was applied to the liver tissue of mice to achieve varying degrees of ablation. Subsequently, the impedance spectra of the biological tissue were measured using an impedance analyzer at different time points before and after ablation, and the equivalent circuit method was used to quantify the results for analysis. We established a neural network model to investigate the relationship between the impedance after ablation and steady-state impedance after 72 h. Using ablation data of 55 mouse livers as training data samples and 5-fold cross-validation, the model predicted the equivalent circuit parameters after 72 h based on the equivalent circuit parameters of the tissues after 30 min of ablation. The model yielded acceptable prediction results with a root mean square error (RMSE) of 7.33, mean absolute percentage error (MAPE) of 8.62%, and coefficient of determination (R