Abstract

Non-invasive fetal saturation prediction is challenging. We propose a multi-detector, inverse modeling, ML based approach. Trained on a large simulated simple tissue model dataset, our generalized NN can estimate simulation parameters given the simulation results. Our model achieves a 9.2% overall validation MSE for tissue model parameters.