A computer-implemented method of searching for a region indicative of a pathology in an image of a portion of an eye acquired by an ocular imaging system, the method comprising: receiving image data defining the image; searching for the region in the image by processing the received image data using a learning algorithm; and in case a region in the image that is indicative of the pathology is found: determining a location of the region in the image; generating an instruction for an eye measurement apparatus to perform a measurement on the portion of the eye to generate measurement data, using a reference point based on the determined location for setting a location of the measurement on the portion of the eye; and receiving the measurement data from the eye measurement apparatus.

A screening platform enables comprehensive ocular evaluations. The screening platform includes a harness that is configured to fit a head of a patient and that includes one or more electronic components that are operable to power an interchangeable module, a central processing unit (CPU) with a graphical processing unit (GPU), and the interchangeable module with communication capabilities to external computational devices, multiple display output devices, and several types of input devices from both an operator and a patient at-hand, wherein the interchangeable module is separable from the screening platform.

Embodiments pertain to a method for characterizing a droopy upper eyelid performed on a computer having a processor, memory, and one or more code sets stored in the memory and executed in the processor. The method may comprise capturing an image of a patient's facial features comprising an eye and a droopy upper eyelid; identifying at least one geometric feature of a pupil of the eye within the image; and determining, based on the at least one geometric feature, whether the droopy upper eyelid is vision impairing or not, or whether the droopy upper eyelid is more likely vision impairing than not vision-impairing.

Provided herein are methods of detecting a ophthalmologic genetic disease in a subject that include matching properties of captured images and/or videos with properties of an ocular pathology model that is trained on a plurality of reference images and/or videos of ocular cells of reference subjects, which properties of the ocular pathology model are indicative of the pathology. Related systems and computer program products are also provided.

Provided herein are methods of detecting a ophthalmologic genetic disease in a subject that include matching properties of captured images and/or videos with properties of an ocular pathology model that is trained on a plurality of reference images and/or videos of ocular cells of reference subjects, which properties of the ocular pathology model are indicative of the pathology. Related systems and computer program products are also provided.

A slit lamp microscope of an aspect example includes a scanner and a data processor. The scanner is configured to scan an anterior segment of a subject's eye with slit light to collect a plurality of cross sectional images. The data processor is configured to generate opacity distribution information that represents a distribution of an opaque area in a crystalline lens, based on the plurality of cross sectional images collected by the scanner.

A slit lamp microscope of an aspect example includes a scanner and a data processor. The scanner is configured to scan an anterior segment of a subject's eye with slit light to collect a plurality of cross sectional images. The data processor is configured to generate opacity distribution information that represents a distribution of an opaque area in a crystalline lens, based on the plurality of cross sectional images collected by the scanner.

Disclosed is a method of identifying dementia by at least one processor of a device. The method includes performing a first task that causes a first object to be displayed on a first region of a screen displayed on a user terminal; and when a preset condition is satisfied, performing a second task that causes at least one object, which induces the user's gaze, to be displayed instead of the first object on the screen of the user terminal.

Disclosed is a method of identifying dementia by at least one processor of a device. The method includes performing a first task that causes a first object to be displayed on a first region of a screen displayed on a user terminal; and when a preset condition is satisfied, performing a second task that causes at least one object, which induces the user's gaze, to be displayed instead of the first object on the screen of the user terminal.

The present invention relates to a method and a device for determining structural progression of an eye disease using an ocular image. According to an embodiment of the present invention, a device for determining structural progression of an eye disease includes a processor, and a memory electrically connected to the processor, wherein, when the processor is executed, the memory stores instructions for obtaining a first-n.sup.th ocular image, which is an n.sup.th first ocular image for a user (where n is a natural number), obtaining a second-n.sup.th ocular image, which is an n.sup.th second ocular image for the user, combining the first-n.sup.th ocular image and the second-n.sup.th ocular image according to a preset method to generate an n.sup.th combined image, and generating an n.sup.th eye disease image for the user by using the n.sup.th combined image and a preset prone area image.