A computer-implemented method of identifying anatomically relevant blood flow characteristics in a patient, the method comprising: receiving, in an electronic storage medium, a patient-specific representation of at least a portion of vasculature of the patient having a lesion at one or more points; receiving one or more observed lumen measurements of the vasculature of the patient; receiving values for one or more metrics of interest associated with one or more locations in the vasculature of the patient; predicting (306, 306A, 306B) one or more healthy lumen measurements of the vasculature of the patient by machine learning or kernel regression using the received values for the one or more metrics of interest; generating a lumen narrowing score based on the observed lumen measurements and the predicted healthy lumen measurements of the vasculature of the patient; and determining (206, 310) the location of a diseased region in the vasculature of the patient based on the generated lumen narrowing score; determining a length or severity of the diseased region, wherein determining the length or severity of the diseased region further comprises: determining one or more locations in the vasculature of the patient that is proximal to and/or distal to the determined location of the diseased region in which an acuity of the diseased region no longer exceeds a predetermined threshold, the acuity of the diseased region being characterized by a lumen narrowing score; generating a visualization of at least the diseased region; outputting one or more selected values of the one or more metrics of interest, where the one or more selected values are associated with the diseased region; and determining a treatment option for the diseased region, based on the one or more outputted selected values. receiving, in an electronic storage medium, a patient-specific representation of at least a portion of vasculature of the patient having a lesion at one or more points; receiving one or more observed lumen measurements of the vasculature of the patient; receiving values for one or more metrics of interest associated with one or more locations in the vasculature of the patient; predicting (306, 306A, 306B) one or more healthy lumen measurements of the vasculature of the patient by machine learning or kernel regression using the received values for the one or more metrics of interest; generating a lumen narrowing score based on the observed lumen measurements and the predicted healthy lumen measurements of the vasculature of the patient; and determining (206, 310) the location of a diseased region in the vasculature of the patient based on the generated lumen narrowing score; determining a length or severity of the diseased region, wherein determining the length or severity of the diseased region further comprises: determining one or more locations in the vasculature of the patient that is proximal to and/or distal to the determined location of the diseased region in which an acuity of the diseased region no longer exceeds a predetermined threshold, the acuity of the diseased region being characterized by a lumen narrowing score; generating a visualization of at least the diseased region; outputting one or more selected values of the one or more metrics of interest, where the one or more selected values are associated with the diseased region; and determining a treatment option for the diseased region, based on the one or more outputted selected values.