Abstract: A method for automatically measuring changes in retinal, retinal pigment epithelial, or choroidal disease includes retrieving a set of images of a fundus and selecting a plurality of images from the set of images. The plurality of images are co-registered and pre-processed such that the quality, contrast, and gain of each of the plurality of images is made similar. Then, a comparison is made between the plurality of images to determine a change in retinal, retinal pigment epithelial, or choroidal disease, wherein the change in retinal, retinal pigment epithelial, or choroidal disease is determined based on various disease metrics. Finally, an indication of the change in retinal, retinal pigment epithelial, or choroidal disease is generated for display to a user on a computing device.

Abstract: A method for for automatically measuring changes in retinal, retinal pigment epithelial, or choroidal disease includes retrieving a set of images of a fundus and selecting a plurality of images from the set of images. The plurality of images are co-registered and pre-processed such that the quality, contrast, and gain of each of the plurality of images is made similar. Then, a comparison is made between the plurality of images to determine a change in retinal, retinal pigment epithelial, or choroidal disease, wherein the change in retinal, retinal pigment epithelial, or choroidal disease is determined based on various disease metrics. Finally, an indication of the change in retinal, retinal pigment epithelial, or choroidal disease is generated for display to a user on a computing device.

Abstract: A method and apparatus for measuring the retinal auto-fluorescence of a subject retina includes an excitation light source for providing an excitation light at a wavelength of at least 450 nm and an image capture device for recording an ocular auto-fluorescence signal generated in response to the excitation light. The image capture device includes a filter for reducing background non-signal wavelengths from the ocular auto-fluorescence signal and an image intensifier for increasing the ocular auto-fluorescence signal strength. The method and apparatus may further include a processor that analyzes the ocular auto-fluorescence signal to determine a contrast change or pattern to thereby detect retinal disease or damage. The processor may compare the images with control images, past images of the same eye or other diagnostic modalities such as fundus photography, angiography, or visual field testing to detect the retinal disease or damage.

Abstract: Disease may be detected, monitored, etc. by detecting metabolic dysfunction in a patient's eyes. In one embodiment of an apparatus, an excitation light is generated by an excitation light source to induce autofluorescence in an ocular tissue (e.g., retinal tissue), wherein the excitation light excites flavoprotein autofluorescence (FA) and minimizes the excitation of non-flavoprotein autofluorescence. At least a single image representing the induced ocular tissue autofluorescence is captured. The at least single image is intensified to increase the signal strength of the ocular tissue autofluorescence. The at least single image is analyzed to generate an indicator of whether a patient has one or more of eye damage, a disease that causes eye damage, or to generate an indicator of the progression of a disease, an indicator of the effectiveness of a treatment, a personalized treatment for a subject, etc.

Abstract: A method and apparatus for measuring the retinal auto-fluorescence of a subject retina includes an excitation light source for providing an excitation light at a wavelength of at least 450 nm and an image capture device for recording an ocular auto-fluorescence signal generated in response to the excitation light. The image capture device includes a filter for reducing background non-signal wavelengths from the ocular auto-fluorescence signal and an image intensifier for increasing the ocular auto-fluorescence signal strength. The method and apparatus may further include a processor that analyzes the ocular auto-fluorescence signal to determine a contrast change or pattern to thereby detect retinal disease or damage. The processor may compare the images with control images, past images of the same eye or other diagnostic modalities such as fundus photography, angiography, or visual field testing to detect the retinal disease or damage.

Abstract: Disease may be detected, monitored, etc. by detecting metabolic dysfunction in a patient's eyes. In one embodiment of an apparatus, an excitation light is generated by an excitation light source to induce autofluorescence in an ocular tissue (e.g., retinal tissue), wherein the excitation light excites flavoprotein autofluorescence (FA) and minimizes the excitation of non-flavoprotein autofluorescence. At least a single image representing the induced ocular tissue autofluorescence is captured. The at least single image is intensified to increase the signal strength of the ocular tissue autofluorescence. The at least single image is analyzed to generate an indicator of whether a patient has one or more of eye damage, a disease that causes eye damage, or to generate an indicator of the progression of a disease, an indicator of the effectiveness of a treatment, a personalized treatment for a subject, etc.

Abstract: A method and apparatus for measuring the retinal auto-fluorescence of a subject retina includes an excitation light source for providing an excitation light at a wavelength of at least 450 nm and an image capture device for recording an ocular auto-fluorescence signal generated in response to the excitation light. The image capture device includes a filter for reducing background non-signal wavelengths from the ocular auto-fluorescence signal and an image intensifier for increasing the ocular auto-fluorescence signal strength. The method and apparatus may further include a processor that analyzes the ocular auto-fluorescence signal to determine a contrast change or pattern to thereby detect retinal disease or damage. The processor may compare the images with control images, past images of the same eye or other diagnostic modalities such as fundus photography, angiography, or visual field testing to detect the retinal disease or damage.