Data-driven-based analysis of coronary index of microcirculatory resistance from angiography data

A computer-implemented method for training a microvasculature health determination system, the method comprising:

• obtaining angiography images of a plurality of vessel inspection regions from different subjects, the angiography images including subsets of angiography images captured over a full contrast agent injection cycle through corresponding vessel inspection regions, the angiography images include subsets of angiography images captured at different perspective views of corresponding vessel inspection regions;
• obtaining vasculature health data for each of the angiography images;
• performing a segmentation on each of the angiography images to generate a segmented image for each angiography image;
• providing the segmented images to a contrast intensity model configured perform a contrast intensity extraction on each of the segmented images to generate a contrast intensity profile of a vessel tree in the vessel inspection region over a sampling time window;
• and
• providing the contrast intensity profile and the vasculature health data to a machine learning model to train the machine learning model to generate a microvasculature health of a vessel tree in a subsequently imaged vessel inspection region.

A method of assessing microvasculature function of a vessel inspection region for predicting a treatment response, the method comprising:

• obtaining angiography images of a vessel tree in the vessel inspection region, the angiography images captured over a sampling time window during which a contrast agent has been injected into the vessel tree, and applying the angiography images to a segmentation model configured to generate segmented images of the vessel tree;
• providing the segmented images to a contrast intensity model and, performing, by the contrast intensity model, a contrast intensity extraction on each of the segmented images to generate a contrast intensity profile of the vessel tree over the sampling time window;
• providing at least a portion of the contrast intensity profile to a microvasculature health model configured to predict a response of the vessel inspection region to a treatment based on a characteristic of the at least a portion of the contrast intensity profile;
• and
• generate an electronic indication of the predicted response to the treatment.