Abstract: A method includes receiving, by a computing device, device information for a user device and user environment devices; sending, by the computing device, image templates to the user device using the device information; adjusting, by the computing device, device settings on the user device and the user environment devices in response to a selection of an image template of the image templates; receiving, by the computing device, an image from the user device; comparing, by the computing device, the image to a target image; determining, by the computing device, the image is acceptable; and in response to determining the image is acceptable, sending, by the computing device, the acceptable image to a third party.

Abstract: A generative adversarial network including a generator portion and a discriminator portion is constructed. The network is configured such that the network operates to enhance intensity images, wherein an intensity image is obtained by illuminating an object with an energy pulse and measuring the return strength of the energy pulse, and wherein a pixel of the intensity image corresponds to the return strength. As a part of the configuring, a loss function of the generative adversarial network is minimized, the loss function comprising a mean square error loss measurement of a noisy intensity image relative to a mean square error loss measurement of a corresponding clean intensity image. An enhanced intensity image is generated by applying the minimized loss function of the network to an original intensity image, the applying improving an image quality measurement of the enhanced intensity image relative to the original intensity image.

Abstract: In an aspect for generating device-specific OCT image, one or more processors may be configured for receiving, at a unified domain generator, first image data corresponding to OCT image scans captured by one or more OCT devices; processing, by the unified domain generator, the first image data to generate second image data corresponding to a unified representation of the OCT image scans; determining by a unified discriminator, third image data corresponding to a quality subset of the unified representation of the OCT image scans having a base resolution satisfying a first condition and a base noise type satisfying a second condition; and processing, using a conditional generator, the third image data to generate fourth image data corresponding to device-specific OCT image scans having a device-specific resolution satisfying a third condition and a device-specific noise type satisfying a fourth condition.

Abstract: Techniques for automatic measurement using structured lights are provided. A computer system uses structured lights to acquire skin information, the skin information being associated with at least one affected area of the skin. The computer system determines parameters related to the at least one affected area of the skin, at least partly, on the skin information and generates an assessment for the at least one affected area of the skin.

Abstract: A method includes receiving, by a computing device, device information for a user device and user environment devices; sending, by the computing device, image templates to the user device using the device information; adjusting, by the computing device, device settings on the user device and the user environment devices in response to a selection of an image template of the image templates; receiving, by the computing device, an image from the user device; comparing, by the computing device, the image to a target image; determining, by the computing device, the image is acceptable; and in response to determining the image is acceptable, sending, by the computing device, the acceptable image to a third party.

Abstract: An eye test administered to a remote subject over a computer network, where sensors provide feedback through the computer network so that light source position, light source brightness and/or a display configuration and/or parameter setting related to a visual display that is used to implement the remote eye test.

Abstract: A method and system of diagnosing a medical condition of a target area of a patient using a mobile device are provided. One or more magnetic field images of a target area of a patient are received. One or more hyperspectral images of the target area of the patient are received. For each of the one or more magnetic field images and the one or more hyperspectral images, a three-dimensional (3D) position of the mobile device is tracked with respect to the target are of the patient. A 3D image of the target area is generated based on the received one or more magnetic field images, one or more hyperspectral images, and the corresponding tracked 3D position of the phone with respect to each image. A medical condition of the target area is diagnosed or monitored based on the generated 3D image.

Abstract: Techniques for automatic association between physical and visual skin properties are provided. A computer system receives a two-dimensional (2D) image and a three-dimensional (3D) image associated with a person and determines surface skin properties using the 2D image and physical skin properties using the 3D image. The computer system generates a skin disorder severity assessment using associations between the surface skin properties and the physical skin properties.

Abstract: Aspects of the invention include a computer implemented method for simulating visual field test results from structural scans, the method includes processing eye image data to extract visual functioning related features. Additionally, generating a representation of a visual function of the eye that is independent of a visual field test (VFT) configuration. Then generating a simulated VFT configuration specific test result based at least in part on the representation.

Abstract: Aspects of the invention include systems and methods that train a teacher neural network using labeled images to obtain a trained teacher neural network, each pixel of each of the labeled images being assigned a label that indicates one of a set of classifications. A method includes providing a set of unlabeled images to the trained teacher neural network to generate a set of soft-labeled images, each pixel of each of the soft-labeled images being assigned a soft label that indicates one of the set of classifications and an uncertainty value associated with the soft label, and training a student neural network with a subset of the labeled images and the set of soft-labeled images to obtain a trained student neural network. Student-labeled images are obtained from unlabeled images using the trained student neural network.

Abstract: In an aspect for generating device-specific OCT image, one or more processors may be configured for receiving, at a unified domain generator, first image data corresponding to OCT image scans captured by one or more OCT devices; processing, by the unified domain generator, the first image data to generate second image data corresponding to a unified representation of the OCT image scans; determining by a unified discriminator, third image data corresponding to a quality subset of the unified representation of the OCT image scans having a base resolution satisfying a first condition and a base noise type satisfying a second condition; and processing, using a conditional generator, the third image data to generate fourth image data corresponding to device-specific OCT image scans having a device-specific resolution satisfying a third condition and a device-specific noise type satisfying a fourth condition.

Abstract: A user profile associated with a first user is received. A user prescription associated with the first user is received. A historical interaction of the first user with a display is received. A global vision model is received. One or more display sets to be used on the display is determined based on at least the user profile, the user prescription, the global vision model, and the historical interaction.

Abstract: A retinal structure and function forecasting method, system, and computer program product include producing an enriched feature representation of clinical measurements and clinical data combined with optical coherence tomography (OCT) data, training a forecasting model with the enriched feature representation, and forecasting a retinal structure at a forecast date based on the trained forecasting model.

Abstract: Aspects of the invention include a computer implemented method for simulating visual field test results from structural scans, the method includes processing eye image data to extract visual functioning related features. Additionally, generating a representation of a visual function of the eye that is independent of a visual field test (VFT) configuration. Then generating a simulated VFT configuration specific test result based at least in part on the representation.

Abstract: Techniques for automatic measurement using structured lights are provided. A computer system uses structured lights to acquire skin information, the skin information being associated with at least one affected area of skin. The computer system determine parameters related to the at least one affected area of the skin, at least partly, on the skin information and generates an assessment for the at least one affected area of the skin.

Abstract: Techniques for automatic association between physical and visual skin properties are provided. A computer system receives a two-dimensional (2D) image and a three-dimensional (3D) image associated with a person and determines surface skin properties using the 2D image and physical skin properties using the 3D image. The computer system generates a skin disorder severity assessment using associations between the surface skin properties and the physical skin properties.

Abstract: Aspects of the invention include systems and methods that train a teacher neural network using labeled images to obtain a trained teacher neural network, each pixel of each of the labeled images being assigned a label that indicates one of a set of classifications. A method includes providing a set of unlabeled images to the trained teacher neural network to generate a set of soft-labeled images, each pixel of each of the soft-labeled images being assigned a soft label that indicates one of the set of classifications and an uncertainty value associated with the soft label, and training a student neural network with a subset of the labeled images and the set of soft-labeled images to obtain a trained student neural network. Student-labeled images are obtained from unlabeled images using the trained student neural network.

Abstract: A method, computer system, and computer program product for real-time pediatric eye health monitoring and assessment are provided. The embodiment may include receiving a plurality of real-time data related to an individual's eye health from a user device. The embodiment may also include assessing biometric indications relating to eye health based on the plurality of real-time data. The embodiment may further include generating a report on the assessed biometric indications. The embodiment may also include collecting clinical information from one or more databases. The embodiment may further include determining whether the assessed biometric indications reach pre-configured threshold conditions. The embodiment may also include generating alerts and recommendations based on analysis of the collected clinical information and the assessed biometric indications based on the assessed biometric indications satisfying the pre-configured threshold conditions.

Abstract: A computer-implemented screening method, system, and computer program product including processing a first sensor scan image at a first time and at a first location to detect a first disassembled component of an object, processing a second sensor scan image at a second time and at a second location to detect a second disassembled component of the object, storing a record of the first disassembled component and the second disassembled component, and flagging the record based on a risk factor of the first disassembled component being combined with the second disassembled component to complete the object.

Abstract: A generative adversarial network including a generator portion and a discriminator portion is constructed. The network is configured such that the network operates to enhance intensity images, wherein an intensity image is obtained by illuminating an object with an energy pulse and measuring the return strength of the energy pulse, and wherein a pixel of the intensity image corresponds to the return strength. As a part of the configuring, a loss function of the generative adversarial network is minimized, the loss function comprising a mean square error loss measurement of a noisy intensity image relative to a mean square error loss measurement of a corresponding clean intensity image. An enhanced intensity image is generated by applying the minimized loss function of the network to an original intensity image, the applying improving an image quality measurement of the enhanced intensity image relative to the original intensity image.