A method for determining a fractional flow reserve classification of an unknown FFR for a stenosis by a data analyzer, comprising: estimating an outlet flow rate of a stenosis based on an effective diameter or radius at an outlet of the stenosis and a diameter or radius at an inlet of the stenosis, extracted from geometry of a tissue interest from extracted from image data of a subject, and a flow rate at the inlet of the stenosis by a boundary condition estimator; estimating a resistance of the stenosis based on aortic blood pressure at the inlet of the stenosis, the outlet flow rate, and a coarse computational fluid dynamics simulation by a boundary condition estimator; classifying by a classifier an unknown fractional flow reserve metric for a cardiac vessel with the stenosis as one of a plurality of different pre-defined classes based on features extracted by a feature extractor and a machine learning model, wherein the classification includes generating a numerical value for the unknown FFR and, further comprising: classifying the unknown fractional flow reserve metric based on the numerical value, wherein the machine learning model is a function that obtains a vector of numbers as input and outputs the numerical value, wherein each element in said vector includes extracted features, wherein the extracted features are extracted by a feature extractor from segmented image data segmented from image data in that includes a representation of the cardiac vessel and the stenosis, wherein features include one or more of a stenosis percentage, a stenosis length, a distance between the aorta and the stenosis, a stenosis position, a heart chamber size, a myocardium mass, a geometry of a coronary artery, a center line of a coronary artery and wherein the extracted features further include one or more estimated boundary conditions of the stenosis, including at least one of the estimated outlet flow rate or the estimated outlet resistance of the stenosis; and generating a signal indicative of the classification. estimating an outlet flow rate of a stenosis based on an effective diameter or radius at an outlet of the stenosis and a diameter or radius at an inlet of the stenosis, extracted from geometry of a tissue interest from extracted from image data of a subject, and a flow rate at the inlet of the stenosis by a boundary condition estimator; estimating a resistance of the stenosis based on aortic blood pressure at the inlet of the stenosis, the outlet flow rate, and a coarse computational fluid dynamics simulation by a boundary condition estimator; classifying by a classifier an unknown fractional flow reserve metric for a cardiac vessel with the stenosis as one of a plurality of different pre-defined classes based on features extracted by a feature extractor and a machine learning model, wherein the classification includes generating a numerical value for the unknown FFR and, further comprising: classifying the unknown fractional flow reserve metric based on the numerical value, wherein the machine learning model is a function that obtains a vector of numbers as input and outputs the numerical value, wherein each element in said vector includes extracted features, wherein the extracted features are extracted by a feature extractor from segmented image data segmented from image data in that includes a representation of the cardiac vessel and the stenosis, wherein features include one or more of a stenosis percentage, a stenosis length, a distance between the aorta and the stenosis, a stenosis position, a heart chamber size, a myocardium mass, a geometry of a coronary artery, a center line of a coronary artery and wherein the extracted features further include one or more estimated boundary conditions of the stenosis, including at least one of the estimated outlet flow rate or the estimated outlet resistance of the stenosis; and generating a signal indicative of the classification.