In this research, a convolutional neural network (CNN) model is developed to predict the RF-induced heating for several tibia plate systems. One thousand four hundred and sixteen device configurations were developed, and the peak 1g-average SAR values were extracted. A subset of the data was used as training set and simulation meshes were used as the input of the CNN model. Results showed a quick network convergence and high correlation. The network also had a low absolute and percentage error level. This demonstrates that one can potentially use CNN model to predict the RF-induced heating of plate systems.
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