A method of laser eye surgery including linking retinal vessel architecture to corneal topography. This enables registration of the steep axis of the cornea in order to orient a toric intraocular lens, and/or to place astigmatic keratotomy incisions. First, a detailed pre-operative retinal image of the vasculature of the retina is obtained. In addition, a pre-operative image of the topography of the eye is obtained. The retinal image is then correlated or superimposed on the topography image to provide a reference. After the patient lies down under the laser eye surgery system, and during the surgery, the retinal vasculature is monitored which provides a reference to the surgery system about the topography of the eye. This process enables registration of the steep axis of the cornea in order to orient a toric intraocular lens and/or to place astigmatic keratotomy incisions.

A method of laser eye surgery including linking retinal vessel architecture to corneal topography. This enables registration of the steep axis of the cornea in order to orient a toric intraocular lens, and/or to place astigmatic keratotomy incisions. First, a detailed pre-operative retinal image of the vasculature of the retina is obtained. In addition, a pre-operative image of the topography of the eye is obtained. The retinal image is then correlated or superimposed on the topography image to provide a reference. After the patient lies down under the laser eye surgery system, and during the surgery, the retinal vasculature is monitored which provides a reference to the surgery system about the topography of the eye. This process enables registration of the steep axis of the cornea in order to orient a toric intraocular lens and/or to place astigmatic keratotomy incisions.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

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 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.

Image processing performed by a processor and including acquiring a two-dimensional fundus image, acquiring a second point on an eyeball model corresponding to at least one first point of the two-dimensional fundus image, and creating data to represent a process to move the first point to the second point.

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.

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.