Abstract: The present disclosure, in some embodiments, relates to a method of determining a stent effectiveness. The method includes accessing a pre-stent intravascular image of a blood vessel of a patient. One or more pre-stent label volumes of the blood vessel are determined and one or more treatment variables associated with the pre-stent intravascular image are determined. One or more FEM-mimic simulations are generated by applying a first deep learning model to the one or more pre-stent label volumes and the one or more treatment variables. The one or more FEM-mimic simulations are used to determine a stent effectiveness metric.

Abstract: The present disclosure, in some embodiments, relates to a method of generating a prognosis for a patient. The method includes accessing automatically segmented pericoronary adipose tissue (PCAT) corresponding to a patient within an electronic memory. A plurality of non-confounding PCAT features are generated by measuring values of Hounsfield units for an imaging unit within the PCAT. The measured values of the Hounsfield units are predominately free of iodine confounding and artifacts. The plurality of non-confounding PCAT features are provided to a regression model.

Abstract: The present disclosure, in some embodiments, relates to a method of predicting stent expansion. The method includes accessing a pre-stent intravascular image of a blood vessel of a patient and segmenting the pre-stent intravascular image to identify a lumen and a calcification lesion. A plurality of features are extracted from one or more of the lumen and the calcification lesion. A regression model is applied to one or more of the plurality of features to determine a minimum stent expansion metric (mSEM). The mSEM indicating how much a stent will expand after implantation. The mSEM is used to generate a classification of the blood vessel as an under-expanded area or a well-expanded area.