A method for hemodynamic quantification in a medical imaging system, the method comprising: scanning a patient with the medical imaging system, the scanning providing cardiac data representing part of a cardiac system of the patient; determining a patient-specific cardiac geometry as a mesh or surface segmented from the cardiac data; extracting values for features of a first input vector of a machine-learnt predictor of the hemodynamic quantification from the patient-specific cardiac geometry, predicting, by the machine-learnt predictor, a value of the hemodynamic quantification in response to the values of the features of the first input vector; extracting values for features of a second input vector of a machine-learnt classifier of uncertainty and/or sensitivity of the hemodynamic quantification; classifying, by the machine-learnt classifier, a value or values of the uncertainty and/or sensitivity of the hemodynamic quantification to the patient-specific cardiac geometry in response to the values for the features of the second input vector; and generating an output based on the value of the hemodynamic quantification and the value or values of the uncertainty and/or sensitivity, wherein extracting (20, 24) the values for the features of the first input vector and the second input vector comprises extracting radii along a vessel represented by the patient-specific cardiac geometry, and wherein predicting comprises predicting the value of the hemodynamic quantification for a first location, wherein classifying comprises classifying for each of a plurality of second locations, and wherein generating the output comprises generating a map of the second locations showing contribution to uncertainty and/or sensitivity to the value of the hemodynamic quantification at the first location. scanning a patient with the medical imaging system, the scanning providing cardiac data representing part of a cardiac system of the patient; determining a patient-specific cardiac geometry as a mesh or surface segmented from the cardiac data; extracting values for features of a first input vector of a machine-learnt predictor of the hemodynamic quantification from the patient-specific cardiac geometry, predicting, by the machine-learnt predictor, a value of the hemodynamic quantification in response to the values of the features of the first input vector; extracting values for features of a second input vector of a machine-learnt classifier of uncertainty and/or sensitivity of the hemodynamic quantification; classifying, by the machine-learnt classifier, a value or values of the uncertainty and/or sensitivity of the hemodynamic quantification to the patient-specific cardiac geometry in response to the values for the features of the second input vector; and generating an output based on the value of the hemodynamic quantification and the value or values of the uncertainty and/or sensitivity, wherein extracting (20, 24) the values for the features of the first input vector and the second input vector comprises extracting radii along a vessel represented by the patient-specific cardiac geometry, and wherein predicting comprises predicting the value of the hemodynamic quantification for a first location, wherein classifying comprises classifying for each of a plurality of second locations, and wherein generating the output comprises generating a map of the second locations showing contribution to uncertainty and/or sensitivity to the value of the hemodynamic quantification at the first location.