Real-Time Radiofrequency Ablation Lesion Depth Estimation Using Multi-frequency Impedance With a Deep Neural Network and Tree-Based Ensembles.

Objective: Design and optimization of statistical models for use in methods for estimating radiofrequency ablation (RFA) lesion depths in soft real-time performance.

Methods: Using tissue multi-frequency complex electrical impedance data collected from a low-cost embedded system, a deep neural network (NN) and tree-based ensembles (TEs) were trained for estimating the RFA lesion depth via regression.

Results: Addition of frequency sweep data, previous depth data, and previous RF power state data boosted accuracy of the statistical models. The root mean square errors were 2 mm for NN and 0.5 mm for TEs for previous statistical models and the root mean square errors were 0.4 mm for NN and 0.04 mm for TEs for the statistical models presented in this paper. Simulation ablation performance showed a mean difference against physical measurements of 0.5 ±0.2 mm for the NN-based depth estimation method and 0.7 ±0.4 mm for the TE-based depth estimation method.

Conclusion: The results show that multi-frequency data significantly improves the depth estimation performance of the statistical models.

Significance: The RFA lesion depth estimation methods presented in this work achieve millimeter-resolution accuracy with soft real-time performance on an ARMv7-based embedded system for potential translation to clinical RFA technologies.