A vision screening device for administering vision screening tests to a patient, to determine the presence of diseases and/or abnormalities in the eye(s) of the patient, is described herein. The vision screening device may include associated methods and systems configured to perform the operations of the vision screening tests. The device may include a radiation source configured to generate near-infrared (NIR) radiation, a sensor configured to capture a grayscale image representing the radiation reflected by the eye(s) of the patient, a white light source, and a camera configured to capture a color image of the eye of the patient. The device may also be configured to generate a composite image based on the grayscale image and/or the color image, determine a difference between a value associated with the eye and an expected value, and generate an output indicative of a condition associated with the eye(s) based on the difference.

A vision screening device for administering vision screening tests to a patient, to determine the presence of diseases and/or abnormalities in the eye(s) of the patient, is described herein. The vision screening device may include associated methods and systems configured to perform the operations of the vision screening tests. The device may include a radiation source configured to generate near-infrared (NIR) radiation, a sensor configured to capture a grayscale image representing the radiation reflected by the eye(s) of the patient, a white light source, and a camera configured to capture a color image of the eye of the patient. The device may also be configured to generate a composite image based on the grayscale image and/or the color image, determine a difference between a value associated with the eye and an expected value, and generate an output indicative of a condition associated with the eye(s) based on the difference.

An eye imaging system can include a head-wearable fundus camera positioning helmet with an outer shell and a conformable liner that can include head location fiducials defining a specified plane. An attached articulating fundus camera fixture can include a fundus camera positioning indication system to indicate a position of the fundus camera with respect to an eye of the patient for acquiring one or more fundus camera images at the indicated position such that fundus camera images recorded over a chronic period of time are assessable using the position information from the fundus camera positioning indication system. The articulating fundus camera fixture can include an articulating arm and a fundus camera mount. The system can assist the patient with helmet positioning, and can automatically position the fundus camera for accurate image capture and analysis, such as using a trained machine learning model for patient evaluation, monitoring, or diagnosis.

An eye imaging system can include a head-wearable fundus camera positioning helmet with an outer shell and a conformable liner that can include head location fiducials defining a specified plane. An attached articulating fundus camera fixture can include a fundus camera positioning indication system to indicate a position of the fundus camera with respect to an eye of the patient for acquiring one or more fundus camera images at the indicated position such that fundus camera images recorded over a chronic period of time are assessable using the position information from the fundus camera positioning indication system. The articulating fundus camera fixture can include an articulating arm and a fundus camera mount. The system can assist the patient with helmet positioning, and can automatically position the fundus camera for accurate image capture and analysis, such as using a trained machine learning model for patient evaluation, monitoring, or diagnosis.

An ophthalmic apparatus includes an illumination optical system including a slit in which a slit-shaped aperture is formed and an iris aperture in which two apertures are formed at positions away from an optical axis position, the iris aperture being arranged at a position substantially conjugate optically to an iris of a subject's eye between a light source and the slit, and configured to generate slit-shaped illumination light using light from the light source and to guide the illumination light to a fundus of the subject's eye; and an imaging optical system including an imaging aperture in which an aperture is formed, and configured to guide returning light of the illumination light to an image sensor, the returning light being guided from the fundus by pupil division and passing through the aperture formed in the imaging aperture. A width of the slit-shaped aperture, a distance between the two apertures, and a size of the aperture in the imaging aperture are set so that an overlap region of a light flux region of the illumination light and a light flux region of the returning light is located on a side of the fundus from a posterior surface of lens of the subject's eye within the eye of the subject eye.

Method and system for a non-invasive assessment of a relation between an intracranial pressure and an intraocular pressure. The method comprising the steps of recording a plurality of images of a retina part of an eye of a person, identifying at least one vein, determining a first plurality of characteristic vein diameters for the identified vein at a first vein location, determining whether the at least one vein has experienced a vein collapse during the first time period, and determining a relation between intraocular pressure and intracranial pressure during the first time period.

Method and system for a non-invasive assessment of a relation between an intracranial pressure and an intraocular pressure. The method comprising the steps of recording a plurality of images of a retina part of an eye of a person, identifying at least one vein, determining a first plurality of characteristic vein diameters for the identified vein at a first vein location, determining whether the at least one vein has experienced a vein collapse during the first time period, and determining a relation between intraocular pressure and intracranial pressure during the first time period.

A microscopy imaging system comprises a fluorescence lifetime imaging microscopy (FLIM) system comprising a pulsed light source configured to direct a plurality of excitation light pulses onto a sample, a photo detector configured to detect emitted fluorescent photons created by the plurality of excitation pulses interacting with the sample, and a FLIM data acquisition system configured to measure the time interval between the excitation light pulses and the detected emitted fluorescent photons, a scanning light microscopy (SLM) system comprising a SLM data acquisition system, a fast scanning mirror and a slow scanning mirror, wherein the mirrors are configured to scan the light pulses across the sample; and a data processing system communicatively connected to the FLIM and SLM systems. Microscopy imaging methods are also disclosed.

A microscopy imaging system comprises a fluorescence lifetime imaging microscopy (FLIM) system comprising a pulsed light source configured to direct a plurality of excitation light pulses onto a sample, a photo detector configured to detect emitted fluorescent photons created by the plurality of excitation pulses interacting with the sample, and a FLIM data acquisition system configured to measure the time interval between the excitation light pulses and the detected emitted fluorescent photons, a scanning light microscopy (SLM) system comprising a SLM data acquisition system, a fast scanning mirror and a slow scanning mirror, wherein the mirrors are configured to scan the light pulses across the sample; and a data processing system communicatively connected to the FLIM and SLM systems. Microscopy imaging methods are also disclosed.

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).