Disclosed herein is a system that uses an eye tracker for diagnosing and facilitating rehabilitation therapy of a patient suffering from disability. The system creates a human machine interface (HMI) that integrates various low cost biosensors and artificial sensors for conducting rehabilitation therapy. The system combines spinal and supra-spinal feedback of the patient with the operant conditioning to facilitate visuomotor balance therapy (VBT), thereby reducing fall risk in disability survivors. The operant conditioning setup for shaping of visuomotor learning to bring about new behavior or to modify a certain aspect of an existing behavior is used for rehabilitation therapy that includes a behaviour response apparatus, a reward delivery module, a stimulus delivery system, and a behaviour control system. The system can also be extended to the patient's home for providing telerehabilitation therapy.

Disclosed herein is a system that uses an eye tracker for diagnosing and facilitating rehabilitation therapy of a patient suffering from disability. The system creates a human machine interface (HMI) that integrates various low cost biosensors and artificial sensors for conducting rehabilitation therapy. The system combines spinal and supra-spinal feedback of the patient with the operant conditioning to facilitate visuomotor balance therapy (VBT), thereby reducing fall risk in disability survivors. The operant conditioning setup for shaping of visuomotor learning to bring about new behavior or to modify a certain aspect of an existing behavior is used for rehabilitation therapy that includes a behaviour response apparatus, a reward delivery module, a stimulus delivery system, and a behaviour control system. The system can also be extended to the patient's home for providing telerehabilitation therapy.

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.

A method for creating a three-dimensional image of an object from a two-dimensional ultrasound video is provided. The method includes acquiring a plurality of two-dimensional ultrasound images of the object and recording a plurality of videos based on the acquired two-dimensional ultrasound images. Each of the videos includes a plurality of frames. The method further includes separating each of the plurality of frames, cropping each of the plurality of frames to isolate structures intended to be reconstructed, selecting a frame near a center of the object and rotating the image to create a main horizontal landmark, and aligning each frame to the main horizontal landmark. The method also includes removing inter-frame jitter by aligning each of the plurality of frames relative to a previous frame of the plurality of frames, reducing the noise of each of the frames, and stacking each of the frames into a three-dimensional volume.

A method for creating a three-dimensional image of an object from a two-dimensional ultrasound video is provided. The method includes acquiring a plurality of two-dimensional ultrasound images of the object and recording a plurality of videos based on the acquired two-dimensional ultrasound images. Each of the videos includes a plurality of frames. The method further includes separating each of the plurality of frames, cropping each of the plurality of frames to isolate structures intended to be reconstructed, selecting a frame near a center of the object and rotating the image to create a main horizontal landmark, and aligning each frame to the main horizontal landmark. The method also includes removing inter-frame jitter by aligning each of the plurality of frames relative to a previous frame of the plurality of frames, reducing the noise of each of the frames, and stacking each of the frames into a three-dimensional volume.

Devices, systems and methods for ocular ultrasound are provided having therapeutic and/or diagnostic applications. In one aspect, an ocular probe is disclosed that is uniquely configured for use in the eye on the basis of shape and frequency. The ocular probe may be multi-functional, providing sensor, optical or other functionality in additional to ultrasound energy.

Devices, systems and methods for ocular ultrasound are provided having therapeutic and/or diagnostic applications. In one aspect, an ocular probe is disclosed that is uniquely configured for use in the eye on the basis of shape and frequency. The ocular probe may be multi-functional, providing sensor, optical or other functionality in additional to ultrasound energy.

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.