ARTIFICIAL INTELLIGENCE FOR PHYSIOLOGICAL QUANTIFICATION IN MEDICAL IMAGING

摘要

For quicker estimation of physiological parameters than using a numerical solution, a machine-learned network is applied. The PV loop may be estimated for a specific patient in real-time without invasive pressure measurements. Synthetic data instead of or in addition to actual patient examples may be used to machine train the network, providing a broader and/or controlled range of examples for more accurate estimation even in rarely occurring pathologies. The synthetic data may be generated by a generative adversarial network.