A Deep Learning-Assisted Hybrid Model for Electric Dosimetry in Electroporation Therapies

Accurate electric dosimetry is essential for predicting treatment outcomes in Irreversible Electroporation (IRE), a promising non-thermal tumor ablation technique. However, real-time 3D electric field computation remains computationally expensive, limiting its practical use in clinical settings. In this study, we propose a hybrid approach combining deep learning (DL)-based initialization with an iterative numerical solver to accelerate dose map calculation. The novelty of our approach is to combine CNN and numerical discretization of the partial differential equation to compute fastly and accurately the electric potential. We evaluate our method on data from 10 patients undergoing IRE liver ablation under real-time clinical conditions. Our results demonstrate a 10-fold speedup compared with conventional solvers while maintaining comparable accuracy. This hybrid method offers a promising pathway toward fast and reliable electric dosimetry for on-line IRE procedures.