Machine learning system for assessing heart valves and surrounding cardiovascular tracts

A computer-implemented machine learning method for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof the method comprising:

• (a) predicting, with a transformation function on a computer, at least one of an unknown anatomical characteristic or an unknown physiological characteristic of at least one of a heart valve, an inflow tract or an outflow tract, using at least one of a known anatomical characteristic or a known physiological characteristic of the at least one heart valve, inflow tract or outflow tract, wherein said at least one of said heart valve, said inflow tract, and said outflow tract includes at least one of said heart valve, a heart ventricle, and a heart atrium;
• and
• (b) using a production mode of a machine learning system on the computer to direct the transformation function and one or more feature vectors, to predict at least one of unknown anatomical characteristic or unknown physiological characteristic of at least one production heart valve, production inflow tract or production outflow tract, based on at least one of patient specific patient known anatomical characteristic or patient known physiological characteristic of the at least one production heart valve, production inflow tract or production outflow tract, wherein said at least one of said production heart valve, said production inflow tract, said production outflow tract includes at least one of said production heart valve, a production heart ventricle, and a production heart atrium, to generate at least one or more quantities of interest.