An ultrasound diagnostic apparatus 1 includes an image acquisition unit 8 that transmits an ultrasound beam from an ultrasound probe 18 to a subject to acquire an ultrasound image, an optic nerve recognition unit 9 that performs image analysis on the ultrasound image acquired by the image acquisition unit 8 to recognize an optic nerve of the subject, an optic nerve evaluation unit 10 that evaluates a shape of the optic nerve of the subject recognized by the optic nerve recognition unit 9 on the basis of an anatomical structure, and an operation guide unit 12 that guides a user to operate the ultrasound probe 18 so as to acquire an ultrasound image for measurement of the optic nerve of the subject on the basis of an evaluation result obtained by the optic nerve evaluation unit 10.

An ultrasound diagnostic apparatus 1 includes an image acquisition unit 8 that transmits an ultrasound beam from an ultrasound probe 18 to a subject to acquire an ultrasound image, an optic nerve recognition unit 9 that performs image analysis on the ultrasound image acquired by the image acquisition unit 8 to recognize an optic nerve of the subject, an optic nerve evaluation unit 10 that evaluates a shape of the optic nerve of the subject recognized by the optic nerve recognition unit 9 on the basis of an anatomical structure, and an operation guide unit 12 that guides a user to operate the ultrasound probe 18 so as to acquire an ultrasound image for measurement of the optic nerve of the subject on the basis of an evaluation result obtained by the optic nerve evaluation unit 10.

The disclosed ultrasound devices may include at least one ultrasound transmitter positioned and configured to transmit ultrasound signals toward a user's face to reflect off a facial feature of the user's face and at least one ultrasound receiver positioned and configured to receive and detect the ultrasound signals reflected off the facial feature. At least one processor may be configured to receive data from the at least one ultrasound receiver and to determine, based on the received data from the at least one ultrasound receiver, at least one of the following eye measurements: an interpupillary distance of the user; an eye relief; or a position of a head-mounted display relative to the facial feature of the user. Various other devices, systems, and methods are also disclosed.

The disclosed ultrasound devices may include at least one ultrasound transmitter positioned and configured to transmit ultrasound signals toward a user's face to reflect off a facial feature of the user's face and at least one ultrasound receiver positioned and configured to receive and detect the ultrasound signals reflected off the facial feature. At least one processor may be configured to receive data from the at least one ultrasound receiver and to determine, based on the received data from the at least one ultrasound receiver, at least one of the following eye measurements: an interpupillary distance of the user; an eye relief; or a position of a head-mounted display relative to the facial feature of the user. Various other devices, systems, and methods are also disclosed.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

Examples relate to a surgical microscope system, and to a system, a method and a computer program for a surgical microscope system. The system comprises one or more processors and one or more storage devices. The system is configured to obtain intraoperative sensor data of at least a portion of an eye from a Doppler-based imaging sensor of the surgical microscope system. The system is configured to process the intraoperative sensor data to determine information on a blood flow within the eye. The system is configured to generate a visualization of the blood flow. The system is configured to provide a display signal to a display device of the surgical microscope system based on the visualization of the blood flow within the eye.

A method and system for selecting an intraocular lens, with a controller having a processor and tangible, non-transitory memory. A plurality of machine learning models is selectively executable by the controller. The controller is configured to receive at least one pre-operative image of the eye and extract, via a first input machine learning model, a first set of data. The controller is configured to receive multiple biometric parameters of the eye and extract, via a second input machine learning model, a second set of data. The first set of data and the second set of data are combined to produce a mixed set of data. The controller is configured to generate, via an output machine learning model, at least one output factor based on the mixed set of data. An intraocular lens is selected based in part on the at least one output factor.

A method and system for selecting an intraocular lens, with a controller having a processor and tangible, non-transitory memory. A plurality of machine learning models is selectively executable by the controller. The controller is configured to receive at least one pre-operative image of the eye and extract, via a first input machine learning model, a first set of data. The controller is configured to receive multiple biometric parameters of the eye and extract, via a second input machine learning model, a second set of data. The first set of data and the second set of data are combined to produce a mixed set of data. The controller is configured to generate, via an output machine learning model, at least one output factor based on the mixed set of data. An intraocular lens is selected based in part on the at least one output factor.

According to examples, systems, devices, and methods for detecting rapid eye movement (REM) are described. The device may include an array of ultrasound sensors oriented to emit transmit ultrasounds signals in an eyeward direction, wherein the ultrasound sensors are to receive a return signal of the transmit signal reflecting off of a target, and wherein the ultrasound sensors are to output a distance signal representative of a distance to a target, the distance signal generated based on the return signal, and a transceiver to receive the distance signals, wherein the transceiver is to transmit the distance signals from the array of ultrasound sensors to a remote device.