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.

A wireless circuit includes a housing, such as a hermetic housing, and at least one antenna coil wound about a coil axis within the housing. The coil axis may be substantially parallel to at least one wall of the housing, wherein the wall parallel to the coil axis is substantially thinner than other walls of the housing.

Apparatus and methods are described for monitoring a subject. A sensor monitors the subject and generates a sensor signal in response thereto and a plurality of filters are used to filter the sensor signal using respective filter parameters. A computer processor receives the sensor signal, filters the signal with each of two or more of the filters, and in response to a quality of each of the filtered signals, selects one of the plurality of filters to filter the sensor signal. Subsequently, the computer processor detects that the subject has undergone motion, by analyzing the sensor signal, and in response thereto, filters the signal with each of two or more of the filters, and in response to a quality of each of the filtered signals, selects one of the plurality of filters to filter the sensor signal. Other applications are also described.

Apparatus and methods are described for monitoring a subject. A sensor monitors the subject and generates a sensor signal in response thereto and a plurality of filters are used to filter the sensor signal using respective filter parameters. A computer processor receives the sensor signal, filters the signal with each of two or more of the filters, and in response to a quality of each of the filtered signals, selects one of the plurality of filters to filter the sensor signal. Subsequently, the computer processor detects that the subject has undergone motion, by analyzing the sensor signal, and in response thereto, filters the signal with each of two or more of the filters, and in response to a quality of each of the filtered signals, selects one of the plurality of filters to filter the sensor signal. Other applications are also described.

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 neural network is trained for estimating patient hemodynamic data using a plurality of extravascular imaging data sets and a plurality of intravascular imaging data sets that are each co-registered to a corresponding extravascular imaging data set. A plurality of hemodynamic data sets are provided, each hemodynamic data set co-registered with the corresponding extravascular imaging data set. The neural network learns what hemodynamic data to expect for a given intravascular imaging data set. An intravascular imaging event is subsequently performed in which an intravascular imaging element is translated within a blood vessel of the patient to produce one or more intravascular images. The neural network uses its training to predict hemodynamic values corresponding to the one or more intravascular images from the intravascular imaging event, and the one or more intravascular images are outputted in combination with the predicted hemodynamic values.

An ultrasonic diagnosis system according to an embodiment of the present disclosure includes a plurality of ultrasonic output patches configured to be attachable to different body parts, a data receiving unit configured to receive data transmitted from the plurality of ultrasonic output patches, a processing unit configured to acquire an imaging result related to the body part by processing the data, and an output unit configured to output the imaging result related to the body part, in which the ultrasonic output patch includes a multichannel ultrasonic transducer array configured to output imaging ultrasonic waves toward the body part and receive the reflected ultrasonic waves, a signal processing module configured to process a signal transmitted to or received from the multichannel ultrasonic transducer array, and a communication module configured to transmit a signal processing result, which is acquired by the signal processing module, to the data receiving unit.

Aspects relate to a portable device that may be used to identify a critical intensity and an anaerobic work capacity of an individual. The device may utilize muscle oxygen sensor data, speed data, or power data. The device may utilize data from multiple exercise sessions, or may utilize data from a single exercise session. The device may additionally estimate a critical intensity from a previous race time input from a user.

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.