An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

A photoacoustic image evaluation apparatus includes a processor configured to acquire a first photoacoustic image generated at a first point in time and a second photoacoustic image generated at a second point in time before the first point in time, the first and second photoacoustic images being photoacoustic images generated by detecting photoacoustic waves generated inside a subject, who has been subjected to blood vessel regeneration treatment, by emission of light into the subject; acquire a blood vessel regeneration index, which indicates a state of a blood vessel by the regeneration treatment, based on a difference between a blood vessel included in the first photoacoustic image and a blood vessel included in the second photoacoustic image; and display the blood vessel regeneration index on a display.

An apparatus for assessing the severity of stenosis in a blood vessel includes an elongate body including a distal portion and a centering assembly. The centering assembly is actuatable to selectively center the elongate body in the vessel. A pressure sensor is disposed adjacent the centering assembly and is configured to detect fluid pressure in the vessel. A processing system receives the measured pressure from the pressure sensor, receives data representing the cross-sectional area of the vessel, receives data representing the size of the distal portion, calculates a offset correlation based on the size of the distal portion and based on the size of the vessel, and calculates a fractional flow reserve (FFR) for the vessel as an index of stenosis severity taking into account the offset correlation and the measured fluid pressure from the pressure sensor.

The systems and methods described herein determine metrics of cardiac or vascular performance, such as cardiac output, and can use the metrics to determine appropriate levels of mechanical circulatory support to be provided to the patient. The systems and methods described determine cardiac performance by determining aortic pressure measurements (or other physiologic measurements) within a single heartbeat or across multiple heartbeats and using such measurements in conjunction with flow estimations or flow measurements made during the single heartbeat or multiple heartbeats to determine the cardiac performance, including determining the cardiac output. By utilizing a mechanical circulatory support system placed within the vasculature, the need to place a separate measurement device within a patient is reduced or eliminated. The system and methods described herein may characterize cardiac performance without altering the operation of the heart pump (e.g., without increasing or decreasing pump speed).

A system may comprise a first catheter having a first steerable segment and a second catheter disposed within the first catheter. The second catheter may have a second steerable segment. The system may also comprise an imaging element supported at a distal end of the second catheter, a coil reference sensor supported at a distal portion of the second catheter, and a processor in electrical communication with the coil reference sensor. The processor may be configured to determine a position of a distal portion of the first catheter with reference to the coil reference sensor.

Systems and methods are disclosed for assessing cardiovascular disease and treatment effectiveness based on adipose tissue. One method includes identifying a vascular bed of interest in a patient's vasculature; receiving a medical image of the patient's identified vascular bed of interest; identifying adipose tissue in the received medical image; receiving a geometric vascular model comprising a representation of the patient's identified vascular bed of interest; and computing an inflammation index associated with the geometric vascular model, using the identified adipose tissue.

There is a need for a technique to determine a presence or absence of morbidity of a lifestyle-related disease and a possibility of future morbidity (risk of morbidity) in a non-invasive manner for a subject. The present disclosure provides a blood abnormality prediction device including, a prediction unit configured to predict a presence or absence of a blood abnormality in a subject on the basis of the information of the image that captures a crown portion of a capillary, wherein the prediction unit is configured to measure one or more selected from the group consisting of an entire width, an apex width, a loop diameter, a venous limb width, and an arterial limb width of the crown portion of the capillary on the basis of the information of the image, to predict the presence or absence of the blood abnormality in the subject from a result of the measurement.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

A system includes a processor circuit configured to receive a set of intravascular data from an intravascular sensor at a first location within a blood vessel. The processor circuit simultaneously receives a set of cardiovascular data from a heart monitor. After the intravascular sensor is moved from the first location to a second location, the processor circuit receives an additional set of intravascular data from the intravascular sensor and an additional set of cardiovascular data from the heart monitor. The processor circuit then determines a distance between the first location and the second location and determines a pulse wave velocity associated with the blood flow within the blood vessel based on the sets of intravascular data, the sets of cardiovascular data, and the distance. The processor circuit then outputs the pulse wave velocity to a display.

A system is provided including an intravascular catheter or guidewire and a processor circuit. The processor circuit determines a metric corresponding to the state of the sympathetic nervous system. The processor circuit then controls the intravascular catheter to alter the blood flow within the vessel. The processor circuit then determines another metric corresponding to the state of the sympathetic nervous system while the blood flow is altered. The processor circuit then provides an output based on the metrics obtained while the blood flow was not altered and while the blood flow was altered.