Cardiac function assessment using machine-learning algorithms

A method of training one or more neural networks, the method comprising:

• configuring a multi-level processing algorithm comprising a multi-task level and a consolidation-task level to determine a fitness of the cardiovascular system of the patient based on at least one input dataset comprising medical measurements of the cardiovascular system, wherein an input of the consolidation-task level is coupled to an output of the multi-task level, wherein the multi-task level is configured to determine multiple diagnostic metrics of the cardiovascular system based on the at least one input dataset, wherein the consolidation-task level is configured to determine the fitness of the cardiovascular system of the patient;
• and
• training the one or more neural networks based on the at least one input dataset and one or more labeling functions, the one or more labeling functions determining a ground truth for the at least one input dataset based on one or more of the following inputs to the one or more labeling functions from the group of:
• a fractional flow reserve measurement;
• a clinical decision;
• a patient outcome;
• a stenosis grade determined based on a coronary angiography;
• and a wall motion score index one or more characteristics of a subsequent clinical procedure of treatment of the cardiovascular system.