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 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.