MRI gradient-fields may cause unintended interactions due to induced voltage potential along elongated conductive components of active implanted medical devices, therefore devices must be tested at clinically-relevant exposure levels. In vivo gradient-induced voltage levels on implanted deep brain stimulation (DBS) systems are simulated in adult human anatomical models. Then, artificial neural network (ANN) models are trained to predict gradient-induced voltages on DBS systems. Predictive ANN models demonstrated good accuracy (RMSE<0.180V). Leave-one-model-out cross-validation results demonstrate that ANN models can perform accurate predictions in general population if a variety of anatomical models representative of the population are included in the training dataset.
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