Assessing heart parameters using neural networks

A system for providing a parameter of a human heart, the system comprising:

• a first trained neural network arranged to:
• receive a plurality of echocardiographic images;
• and
• identify at least one of a two-chamber view or a four-chamber view in the plurality of echocardiographic images;
• a third responder arranged to:
• select, based on at least one characteristic of the echocardiographic images, a subset of images for a given view, wherein the at least one characteristic is at least one of gain, granularity, resolution, level of motion in the image, the field-of-view, the size of the field-of view, the size of the image, or the number of chambers in the given view;
• generate a first output based on the subset of images;
• a second trained neural network arranged to:
• receive the first output;
• contour the first output by identifying the endocardial border of the left ventricle for the given view;
• and
• generate a second output based on the contoured first output;
• a first responder arranged to:
• receive the second output;
• identify end-systole and end-diastole images based on the second output;
• and
• generate a third output based on the end-systole and end-diastole images;
• a second responder arranged to:
• receive the third output;
• and
• derive at least one first parameter of the heart based on the third output, wherein the at least one first parameter is Global Longitudinal Stain, principal strain, shear strain or regional strain.