The present disclosure relates to technology for estimating bio-information by using a foldable electronic device. The foldable electronic device includes a main body part, having a first main body and a second main body, configured to fold along a folding line; a first sensor provided on the first main body, and configured to obtain a contact image of an object of a user; a second sensor provided on the first main body, and configured to measure a degree of folding of the main body part; and a processor configured to estimate bio-information of the user, based on the contact image of the object and the degree of folding.

A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.

An apparatus and method for estimating one or more hemodynamic parameters such as cardiac output or stroke volume. Embodiments are based on the concept of incorporating information about vascular tone into hemodynamic parameter estimation to improve accuracy. More particularly, embodiments use a measurement of a time duration for a blood pulse to travel from the heart along a certain length of an arterial path as a proxy measure for vascular tone, and incorporate this into hemodynamic parameter estimation. Embodiments are also based on incorporating vascular tone proxy measurements for multiple different arterial paths to take account of vascular tone variations between different portions of the circulatory system.

A method for determining an intracranial hemodynamic parameter according to embodiments of the present disclosure is provided, which includes determining a three-dimensional a model of a blood vessel based on CT angiographic data, and determining at least one of a boundary condition of each inlet or a boundary condition of each outlet in the three-dimensional vessel model based on CT perfusion imaging data and CT angiographic data.

A device for diagnosing an abnormality by measuring a minimal change in a muscle according to an embodiment includes a vibration unit that provides vibration to a body part of a user; a measuring unit that detects a minimal change in a muscle by measuring a change in elasticity of the body part according to the vibration; and a processing unit that calculates an abnormality of the body part based on the change in the elasticity of the body part, wherein the processing unit calculates the abnormality of the body party by using an algorithm that detects a degree to which a specific value of the muscle is far from a distribution chart by analyzing data distribution or an anomaly detection algorithm that detects whether there is anomaly in a variable.

A method for detecting or monitoring respiratory or cardiac health of a patient includes measuring any intravascular or intracardiac pressure (IVP) of a patient over a period of time, said IVP including a measured respiratory wave, defining respiratory effort of the patient as a peak-to-peak amplitude of said respiratory wave, and using the respiratory effort to detect or monitor respiratory and cardiac health of the patient by comparing the respiratory effort with a known value of respiratory effort or by monitoring changes in the respiratory effort of the patient over another period of time.

An apparatus for determining a functional index for stenosis assessment of a vessel is provided. The apparatus comprises an input interface (40) and a processing unit (50). The input interface is configured to obtain image data (30) representing a two-dimensional representation of a vessel (6). The processing unit (50) is configured to determine a course of the vessel (6) and a width (w1, w2) of the vessel along its course in the image data and is further configured to determine the functional index for stenosis assessment of the vessel based on the width of the vessel in the image data.

The present invention provides a carotid blood pressure detection device, comprising: a first sensing unit, a second sensing unit, and a controller connected or coupled to the first sensing unit and the second sensing unit. The first sensing unit is disposed on a subject's neck and adjacent to a first position of the subject's carotid arteries. The second sensing unit is disposed on the subject's neck and adjacent to a second position of the subject's carotid arteries. The controller derives a mean arterial pressure of a section of the subject's carotid arteries that lies between the first position and the second position of the subject's carotid arteries from pulse wave data measured and obtained by the first sensing unit and pulse wave data measured and obtained by the second sensing unit.

Methods and systems are disclosed for creating and using a neural network model to estimate a cardiac parameter of a patient, and using the estimated parameter in providing blood pump support to improve patient cardiac performance and heart health. Particular adaptations include adjusting blood pump parameters and determining whether and how to increase or decrease support, or wean the patient from the blood pump altogether. The model is created based on neural network processing of data from a first patient set and includes measured hemodynamic and pump parameters compared to a cardiac parameter measured in situ, for example the left ventricular volume measured by millar (in animals) or inca (in human) catheter. After development of a model based on the first set of patients, the model is applied to a patient in a second set to estimate the cardiac parameter without use of an additional catheter or direct 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, 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.