Abstract: System/Method/Device for labelling images in an automated manner to satisfy a performance of a different algorithm and then applying active learning to learn a deep learning model which would enable ‘real-time’ operation of quality assessment and with high accuracy.

Abstract: A head-mounted display (HMD) determines whether a set of criteria is satisfied based on eye-related readings collected by a camera of the HMD and wirelessly transmits a set of messages in response to a determination that the set of criteria is satisfied. An attachment includes a set of light-emitting diodes (LEDs) and activates the set of LEDs to emit the light at the bleaching intensity in response to receiving the set of messages.

Abstract: A head-mounted display and accessory system in which the accessory emits bleaching light through the head-mounted display to bleach photoreceptors for dark adaptation testing includes a casing, where a lens of a head-mounted display is positioned inside the cavity, and where an aperture of the casing is aligned with an exterior-facing camera of the head-mounted display. The system also includes an attachment body, where a posterior end of the attachment body is fixed to an anterior end of the casing, and where the attachment body includes a set of light-emitting diodes (LEDs), where the set of LEDs is attached to the posterior end of the attachment body and is directed towards the lens. The system also includes a circuitry that is configured to control light emission of the set of LEDs.

Abstract: An ophthalmic imaging system provides an automatic focus mechanism based on the difference of consecutive scan lines. The system also provides of user selection of a focus point within a fundus image. A neural network automatically identifies the optic nerve head in an FA or ICGA image, which may be used to determine fixation angle. The system also provides additional scan tables for multiple imaging modalities to accommodate photophobia patients and multi-spectrum imaging options.

Abstract: An OCT system includes a machine learning (ML) model trained to receive a single OCT scan/image and provide an image translation and/or denoise function. The ML model may be based on a neural network (NN) architecture including a series of encoding modules in a contracting path followed by a series of decoding modules in an expanding path leading to an output convolution module. An intermediate error module determines a deep error measure, e.g., between a training output image and at least one encoding module and/or decoding module, and an error from the output convolution module is combined with the deep error measure. The NN may be trained using true averaged images as ground truth, training outputs. Alternatively, the NN may be trained using randomly selected, individual OCT images/scans as training outputs.

Abstract: Various systems and methods for improved OCT angiography imaging are described. An example method of identifying intraretinal fluid in optical coherence tomography (OCT) image data of an eye includes collecting OCT image data using an OCT system. The data includes at least one cluster scan containing OCT image data collected at approximately same set of locations on the sample. A first motion contrast image is generated by applying a first OCT angiography processing technique to the cluster scan to highlight motion contrast in the sample. A second motion contrast image is generated by applying a second OCT angiography processing technique to the cluster scan to highlight motion contrast in the sample. An image displaying intraretinal fluid in the eye is generated using the first and second motion contrast images and then displayed or stored or a further analysis thereof.

Abstract: Various systems and methods for improved OCT angiography imaging are described. An example method of identifying intraretinal fluid in optical coherence tomography (OCT) image data of an eye includes collecting OCT image data using an OCT system. The data includes at least one cluster scan containing OCT image data collected at approximately same set of locations on the sample. A first motion contrast image is generated by applying a first OCT angiography processing technique to the cluster scan to highlight motion contrast in the sample. A second motion contrast image is generated by applying a second OCT angiography processing technique to the cluster scan to highlight motion contrast in the sample. An image displaying intraretinal fluid in the eye is generated using the first and second motion contrast images and then displayed or stored or a further analysis thereof.

Abstract: The subject invention relates to combinatorial analyses of data from two or more diagnostic tests for the detection of eye diseases, simplified interpretation of test results, and assessment of disease stage and rate of change. Of particular interest is to develop combinatorial analyses to improve glaucoma detection and progression rate assessment based on combinations of structural and functional tests. More specifically, approaches are described where data of one or more tests and their normative database are converted to the distribution and scale of another test for further analysis to detect glaucomatous damage; approaches are also described where data of more than one tests are used to assess stage index and rate of change; in addition, methods for displaying the combinatorial analysis results are disclosed.