The present disclosure is directed to systems and methods that leverage machine learning for detection of eye or non-eye (e.g., systemic) diseases from external anterior eye images. In particular, a computing system can include and use one or more machine-learned disease detection models to provide disease predictions for a patient based on external anterior eye images of the patient. Specifically, in some example implementations, a computing system can obtain one or more external images that depict an anterior portion of an eye of a patient. The computing system can process the one or more external images with the one or more machine-learned disease detection models to generate a disease prediction for the patient relative to one or more diseases, including, as examples, diseases which present manifestations in a posterior of the eye (e.g., diabetic retinopathy) or systemic diseases (e.g., poorly controlled diabetes).

Disclosed is a method of identifying dementia using at least one processor of a device according to some embodiments of the present disclosure. More particularly, the method may include performing a first task of causing for a user terminal to display a first screen including a sentence; performing a second task of causing for the user terminal to acquire an image including user’s eyes in association with displaying a moving object instead of the first screen; and performing a third task of causing for the user terminal to acquire a recording file in association with displaying a second screen in which the sentence is hidden, wherein the first task includes a sub-task of causing color of at least one word constituting the sentence included in the first screen to be sequentially changed.

Systems and methods are presented for providing a machine learning model for segmenting an optical coherence tomography (OCT) image. A first OCT image is obtained, and then labeled with identified boundaries associated with different tissues in the first OCT image using a graph search algorithm. Portions of the labeled first OCT image are extracted to generate a first plurality of image tiles. A second plurality of image tiles is generated by manipulating at least one image tile from the first plurality of image tiles, such as by rotating and/or flipping the at least one image tile. The machine learning model is trained using the first plurality of image tiles and the second plurality of image tiles. The trained machine learning model is used to perform segmentation in a second OCT image.

Embodiments relate to extracting blood vessel geometry from one or more optical coherent tomography (OCT) images for use in analyzing biological structures for diagnostic and therapeutic applications for diseases that can be detected by vascular changes in the retina. An OCT image refers generally to one or more images of any dimension obtained using any one or combination of OCT techniques. Some embodiments include a method of identifying a region of interest of a retina from a plurality of retinal blood vessels in at least one optical coherence tomography (OCT) image of at least a portion of the retina. Some embodiments include a method of distinguishing between a plurality of retinal layers from vessel morphology information of retinal blood vessels in at least one optical coherence tomography (OCT) image of at least a portion of the retina.

Machine learning technologies are used to identify and separating abnormal and normal subjects and identifying possible disease types with images (e.g., optical coherence tomography (OCT) images of the eye), where the machine learning technologies are trained with only normative data. In one example, a feature or a physiological structure of an image is extracted, and the image is classified based on the extracted feature. In another example, a region of the image is masked and then reconstructed, and a similarity is determined between the reconstructed region and the original region of the image. A label (indicating an abnormality) and a score (indicating a severity) can be determined based on the classification and/or the similarity.

Generation of treatment recommendations for topographic-based excimer laser surgical procedures is described that includes generating accurate cylinder compensation and spherical compensation values that are adjusted to compensate for unique characteristics of topographic-based excimer laser surgical systems. Generating treatment recommendations generally includes determining a topographic vector, a posterior astigmatism vector and an anterior astigmatism vector, and generating an internal astigmatism vector using the topographic vector, the posterior astigmatism vector, the anterior astigmatism vector, and a manifest astigmatism vector. In embodiments, the cylinder compensation is generated using multiple vectors while subtracting the internal astigmatism vector and the posterior astigmatism vector which remain in the eye after treatment, and the spherical compensation is generated using an initial spherical compensation modified by addback modifiers and a regression analysis nomogram. In procedures where the corneal epithelium is removed, an epithelial refractive vector is determined from an epithelial thickness/topography map and added to the other vectors.

A method for segmenting images is provided including tessellating an image obtained from one of an image database and an imaging system into a plurality of sectors; classifying each of the plurality of sectors by applying one or more pre-defined labels to each of the plurality of sectors, wherein the pre-defined labels indicate at least one of an image quality metric (IQM) and a metric of structure; assigning each of the plurality of classified sectors an Image Quality Classification (IQC); identifying anchor sectors among the plurality of classified sectors, applying filtering and edge detection to identify target boundaries; applying contouring across contiguous sectors and using the assigned IQC as a guide to complete segmentation of an edge between any two identified anchor sectors; and smoothing across segmented regions to increase parametric second-order continuity.

The present disclosure provides systems and methods for diagnosing disease. In some aspects, an imaging system is provided that includes a light source configured to illuminate a retina of the eye with light, one or more imaging devices configured to receive light returned from the retina to generate one or more spatial-spectral images of the retina, and a computing device configured to receive the one or more spatial-spectral images of the retina, evaluate the one or more spatial-spectral images, and identify one or more biomarkers indicative of a neurogenerative pathology.

An ophthalmic apparatus includes an illumination optical system that generates slit-shaped illumination light using a first light source; an optical scanner that deflects the illumination light to a fundus of a subject's eye; an imaging optical system that captures light from the fundus using a rolling shutter method; an acquisition unit that acquires a fundus image of the subject's eye using light from a second light source; a flare determination unit that determines whether or not flare occurs by analyzing the fundus image; a controller that performs flare optimization control by controlling at least one of the first light source, the illumination optical system, the optical scanner, the imaging optical system, and the image sensor based on a first determination result obtained by the flare determination unit; and an image forming unit that forms an image of the fundus when the flare does not occur.

The present application relates to a method of acquiring a side image for analyzing the degree of ocular proptosis. According to an embodiment, an image acquisition method is provided which is including: acquiring a front image of the subject's face while guidance is

Oven to satisfy a first photographing condition; generating panorama guidance on the basis of position information of a first point and a second point extracted from the front image; providing guidance on movement of a photographing device to acquire a preview image corresponding to the panorama guidance; and acquiring a side image of the subject's face while guidance is given to satisfy a second photographing condition. The first captured image shows iris areas of the subject, and the second captured image shows an outer canthus and a cornea of one of the subject's eyes.