Abstract: Certain embodiments provide a method of performing ophthalmic surgical procedures. The method includes ingesting and preparing pre-operative data and intra-operative data associated with a patient's eye for further processing. In certain embodiments, the method further includes integrating the pre-operative data and intra-operative data to generate context sensitive data for further processing. The method also includes classifying and annotating the pre-operative data, the intra-operative data, and the context sensitive data. The method also includes extracting one or more actionable inferences from the pre-operative data, the intra-operative data, context sensitive data, and the classified and annotated data. The method further includes triggering, based on the one or more actionable inferences, one or more actions on an imaging system or a surgical system.

Abstract: Systems and methods for determining a compatibility between a multi-focal contact lens and a patient seeking presbyopia vision correction include receiving, from a first device associated with a first eye-care professional (ECP), a request for selecting a contact lens for a consumer, wherein the request comprises biometric information associated with the consumer; obtaining a performance metric associated with the first ECP; determining, using the machine learning model and based on the performance metric, a customized compatibility index indicating a compatibility between a particular contact lens and the consumer for the first ECP; and presenting a report indicating the compatibility index on the first device. Additional systems, methods, and non-transitory machine-readable mediums are also provided.

Abstract: In certain embodiments, a system, a computer-implemented method, and computer-readable medium are disclosed for light efficient fluorescence imaging. The retina is flashed with broadband light and returned light is imaged after passing through one or more filters, such as notch filers, low-pass filters, and high-pass filters. Images may be captured with a single camera or at least two cameras, one capturing transmitted light from the filter and the other capturing returned light. Images may be combined by subtraction and/or addition to obtain a combined image representing light within a passband whereas no passband filters are used during imaging.

Abstract: In certain embodiments, a system, a computer-implemented method, and computer-readable medium are disclosed for performing integrated analysis of MSI and OCT images to diagnose eye disorders. MSI and OCT are processed using separate input machine learning models to create input feature maps that are input to an intermediate machine learning model. The intermediate machine learning model processes the input feature maps and outputs a final feature map that is processed by one or more output machine learning models that output one or more estimated representations of a pathology of the eye of the patient.

Abstract: In certain embodiments, a system, a computer-implemented method, and computer-readable medium for generating multispectral imaging (MSI) based on performing an analytical MSI operation are described. The analytical MSI operation includes synchronizing a scan of a target by a broadband imaging device with a generation of a plurality of light signals, from a plurality of broadband illumination sources, directed towards the target. A set of spectral information associated with the target is generated, based on the scan of the target with the broadband imaging device. A set of multispectral imaging (MSI) information associated with the target is generated, based on performing an MSI operation using at least the set of spectral information. Ophthalmic information is determined based on the set of MSI information.

Abstract: A system and method of assessing vision quality of an eye is presented, with a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The controller is configured to selectively execute at least one machine learning model. Execution of the instructions by the processor causes the controller to: receive wavefront aberration data of the eye and express the wavefront aberration data as a collection of Zernike polynomials. The controller is configured to obtain a plurality of input factors based on the collection of Zernike polynomials. The plurality of input factors is fed into the at least one machine learning model, which is trained to analyze the plurality of input factors. The machine learning model generates at least one vision correction factor based in part on the plurality of input factors.

Abstract: Particular embodiments disclosed herein provide an alignment guide for aligning a toric IOL during surgery. An image with a reference axis is obtained, such as from a digital microscope, and processed, such as using an autoencoder, to label alignment marks on the IOL and possibly other features of the IOL. The label is processed, such as using a logistic regression model, to estimate an IOL axis of the IOL intersecting the alignment marks. An output image is generated from the image that has superimposed thereon guides to a surgeon, such as a line representing the IOL axis, a rotation direction indicator, and a number or other representation of a difference between the reference axis and the IOL axis. Tracking of features of the IOL may be performed across multiple images to predict the location of features not represented in a particular image.

Abstract: In certain embodiments, an ophthalmic system and computer-implemented method for analyzing multiple spectral information to generate ophthalmic information are described. In an exemplary ophthalmic system, multiple spectral information associated with an eye of a patient is captured via an imaging system. A first set of information and a second set of information are extracted from the multiple spectral information. A visualization of the multiple spectral information is generated using the first set of information. The first set of information and the second set of information are evaluated using different deep learning models to generate ophthalmic information. The ophthalmic information is sent to a user for diagnostic evaluation.

Abstract: Certain embodiments provide a method of performing ophthalmic surgical procedures. The method includes ingesting and preparing pre-operative data and intra-operative data associated with a patient's eye for further processing. In certain embodiments, the method further includes integrating the pre-operative data and intra-operative data to generate context sensitive data for further processing. The method also includes classifying and annotating the pre-operative data, the intra-operative data, and the context sensitive data. The method also includes extracting one or more actionable inferences from the pre-operative data, the intra-operative data, context sensitive data, and the classified and annotated data. The method further includes triggering, based on the one or more actionable inferences, one or more actions on an imaging system or a surgical system.

Abstract: In certain embodiments, an ophthalmic system and computer-implemented method for performing ophthalmic image registration are described. The ophthalmic image registration includes obtaining a plurality of images of an eye of a user. Within each image of the plurality of images, a segmented region(s) of the eye within the image is determined based on evaluating the image with a neural network(s), and a set of point features of the eye within the segmented region(s) of the eye is determined based on evaluating the image with the neural network(s). A set of transformation information for transforming at least one of the plurality of images is generated based on performing one or more image processing operations on the set of point features within each image of the plurality of images. At least one of the plurality of images is transformed, based on the set of transformation information.

Abstract: Systems and methods are presented for providing a vision simulation of a patient who has an eye condition. A composite image representing a real-world scene is obtained. The composite image includes multiple image layers, where each image layer represents objects that are at a particular viewing distance in the real-world scene. A first eye model representing the eye optics of the patient is generated. A second eye model representing the eye optics of a viewer is generated. The second eye model is modified by performing a mathematical function. A simulated image representing the vision of the patient is generated by convolving the first eye model and the modified second eye model with the composite image. In some embodiments, a tone mapping algorithm may also be applied to the simulated image to simulate a nighttime scene.

Abstract: Certain aspects of the present disclosure provide an ophthalmic imaging device for enhancing images. The device comprises an image capture component configured to generate an image stream comprising a first frame preceding a second frame, both capturing a branch of veins in an eye. The device further comprises an image processor configured to calculate first order statistics for a plurality of blocks for the first frame and to interpolate first order statistics for the first frame based at least in part on the branch of veins. The image processor is also configured to generate a tone mapping function and calculate tone mapping values for individual pixels of the second frame based on the tone mapping function. The image processor is additionally configured to generate an enhanced frame based on the calculated tone mapping values for individual pixels of the second frame to the pixels of the second frame.

Abstract: A personalized assistance system for a user of a vision correction device includes a remote computing unit with a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The controller is configured to selectively execute one or more machine learning models. A user device is operable by the user and includes an electronic diary module configured to prompt the user to answer one or more preselected questions at specific intervals. The electronic diary module is configured to store respective answers, entered by the user in response to the one or more preselected questions, as self-reported data. The controller is configured to obtain the self-reported data from the electronic diary module and generate an analysis of the self-reported data, via the one or more machine learning models. The controller is configured to assist the user based in part on the analysis.

Abstract: 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.

Abstract: A system and method for visually enhancing an original image of an eye includes a visualization module. A controller is configured to convert an output of the visualization module to a first pixel cloud in a first color space and map the first pixel cloud to a second pixel cloud in a second color space. The method includes identifying at least one selected zone in the second color space. The controller is configured to move the selected zone from an original location to a modified location in the second color space. The second pixel cloud is updated to obtain a modified second pixel cloud, which is transformed into a third pixel cloud in the first color space. An enhanced image is formed based in part on the modified second pixel cloud and provides selective visual enhancement in the selected zone without affecting contrast in a remainder of the original image.

Abstract: A system and method for augmenting an original OCT (optical coherence tomography) image includes a controller having a processor and a tangible, non-transitory memory on which instructions are recorded. The system includes one or more learning modules selectively executable by the controller. The learning modules are trained by a training network with a training dataset having a plurality of training ultrasound bio-microscopy images and respective training OCT images. Execution of the instructions by the processor causes the controller to obtain the original OCT image, captured through an OCT device. The controller is configured to generate an augmented OCT image based in part on the original OCT image, by executing the (trained) learning modules. The augmented OCT image at least partially extends a peripheral portion of the original OCT image.

Abstract: Systems and methods for assisting in the removal of a cataract from an eye can include obtaining pre-operative data for the eye, the pre-operative data including imaging data associated with the lens of the eye, determining a lens density map based on the imaging data associated with the lens, and generating laser fragmentation patterns for a laser fragmentation procedure based on the lens density map.

Abstract: A personalized assistance system for a user of a vision correction device includes a remote computing unit with a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The controller is configured to selectively execute one or more machine learning models. A user device is operable by the user and includes an electronic diary module configured to prompt the user to answer one or more preselected questions at specific intervals. The electronic diary module is configured to store respective answers, entered by the user in response to the one or more preselected questions, as self-reported data. The controller is configured to obtain the self-reported data from the electronic diary module and generate an analysis of the self-reported data, via the one or more machine learning models. The controller is configured to assist the user based in part on the analysis.

Abstract: A system and method of assessing vision quality of an eye is presented, with a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The controller is configured to selectively execute at least one machine learning model. Execution of the instructions by the processor causes the controller to: receive wavefront aberration data of the eye and express the wavefront aberration data as a collection of Zernike polynomials. The controller is configured to obtain a plurality of input factors based on the collection of Zernike polynomials. The plurality of input factors is fed into the at least one machine learning model, which is trained to analyze the plurality of input factors. The machine learning model generates at least one vision correction factor based in part on the plurality of input factors.

Abstract: Systems and methods are presented for providing a vision simulation of a patient who has an eye condition. A composite image representing a real-world scene is obtained. The composite image includes multiple image layers, where each image layer represents objects that are at a particular viewing distance in the real-world scene. A first eye model representing the eye optics of the patient is generated. A second eye model representing the eye optics of a viewer is generated. The second eye model is modified by performing a mathematical function. A simulated image representing the vision of the patient is generated by convolving the first eye model and the modified second eye model with the composite image. In some embodiments, a tone mapping algorithm may also be applied to the simulated image to simulate a nighttime scene.