A Machine Learning-Based Prediction of RF Transfer Function of Active Implantable Medical Devices in Homogeneous and Heterogeneous Tissue

Both the Tier-3 and Tier-4 methods proposed in ISO/TS 10974 for assessing the radiofrequency safety of active implantable medical devices have limitations in practice. The transfer function proposed in the Tier-3 method can only be measured individually for each implant and only for homogeneous tissue environments, while Tier-4 requires significant high-speed computing resources. In this study, machine learning algorithms are proposed to predict the transfer function both in homogeneous and heterogeneous tissue environment. The results show that this approach is feasible and performs well in the prediction task.