A method of evaluating a joint includes obtaining test data indicative of movement of the joint during a test of the joint, generating visualization data for a three-dimensional representation of the joint to be rendered via a display, generating plane data for a representation of a plane to be rendered via the display with the three-dimensional representation of the joint, the plane having a position and an orientation fixed relative to a bone of the joint, adjusting the visualization data to animate the three-dimensional representation to depict, via the display, the movement of the joint during the test, and adjusting the plane data to update the position and the orientation of the plane in accordance with the movement of the joint.

An apparatus for performing a remote test of range of motion of a person operating a user device includes a transceiver, a processor, and a display. The transceiver is configured to transmit a link to the user device and to receive motion data from the user device. The processor is configured to calculate in real time, based on the motion data, the position of the user device to enable real-time display to a test provider of the performance of the test and to determine in real time the quality of the test. The display is configured to show in real time a continuous indication of the performance of the test and quality results of the test. A method for performing a remote test of range of motion of a person operating a user device is also described and claimed.

Segmentation of bony regions in MRI images of joints is automated using a two-stage process. In a first stage, a machine-learning image-slice categorizer is used to categorize image slices of the MRI image data as belonging to one of a set of image-slice categories, depending on presence or absence of bone and/or tendon in the image slice. In a second stage, a first instance of a machine-learning segmentation classifier is used to segment image slices that contain both bone and tendon into bone and non-bone regions, and a second instance of a machine-learning segmentation classifier is used to segment image slices that contain bone but not tendon into bone regions and non-bone regions. Results from the two segmentation classifiers can be combined across image slices to provide a final segmentation of the bony structures, including inflammatory regions, in the image data.

A system for measuring and monitoring physiologically relevant motion of a subject includes at least a motion sensor to measure movement of the subject and produce a series of movement data representing the movement of the subject over a period of time. The system also includes at least a biometric sensor to simultaneously measure biometrics of the subject and produce a series of biometric values of the subject over the period of time. The system is configured to determine a noise-to-signal ratio for the series of movement data as a function of biometric intervals in the series of biometric values and identify at least a portion of the series of movement data as corresponding to a physiologically relevant biorhythm. The system can be used to diagnose and monitor a disease or disorder, including a neurological disorder or a traumatic brain injury.

A device, system and method for monitoring carpal tunnel syndrome (“CTS”). The device comprises a body configured to be worn by a user, sensors, a processor, a vibration mechanism, and a power source. The sensors monitor a position of the user's hand to prevent CTS. The processor of the device is configured to determine if the user's hand is in a CTS position and the processor is configured to generate an alert signal to alert the user to the CTS position.

An apparatus comprising at least one sensor to detect the position and/or orientation of a body portion of a subject, the sensor in communication with a computing device to process sensor data and optionally a transmitter to transmit sensor data between the sensor and the computing device and/or one or more computing devices.

Disclosed is a ultrasonic diagnostic apparatus an a method of controlling the same, capable of allowing a sonographer to take an appropriate rest by measuring the usage time and the angle of bend of the probe and assigning weights to the usage time and the angle of bend of the probe such that the wrist fatigue of a sonographer is determined, and warning the sonographer about the determined wrist fatigue, the ultrasonic diagnostic apparatus including a probe configured to transmit an ultrasonic signal to a target object and receive a ultrasonic signal reflected from the target object; a storage configured to store a weight that is determined on the basis of a type of the probe and an application of the probe; and a controller configured to determine a wrist fatigue of a user who uses the probe on the basis of the stored weight and a usage time of the probe.

A method of a measuring kinematic parameter in a subject is provided. The method includes obtaining a first magnetic resonance (MR) image set of a bone marrow segment of the subject in a first position and obtaining a second MR image set of the bone marrow segment of the subject in a second position where the second position different from the first position. The method further includes registering the first image set with the second image set and measuring a kinematic parameter.

Methods for evaluating subjects having conditions associated with loss of muscle function (e.g., a motor neuron disease, a neuromuscular disease, or a myopathy) by measuring muscle function (e.g., muscle strength) are disclosed.

A bone density estimating method, comprising: acquiring, by an MR scanning device, a magnetic resonance, MR, sequence of a body portion, wherein the MR sequence comprises quantitative information of the body portion; generating, by a processing circuit, an MR image of the body portion based on the MR sequence, wherein each voxel of the MR image represents a volume of the body portion; identifying, by the processing circuit, a part of the MR image representing a bone portion of the body portion; for a voxel of the identified part of the MR image, estimating a bone density of a volume of the bone portion represented by the voxel, based on a quantitative value of the voxel. The quantitative information of the body portion comprises a proton density.