Clinical-quality eye examinations are provided at locations remote from the physician and outside of a clinical setting. A portable optical device and corresponding software are delivered to the patient's location. The portable optical device is configured for patient use in self-conducting an eye or vision exam and taking optical measurements. As a function of type of optical measurement data or health consideration of the patient, the corresponding software provides uploading and live streaming of the optical measurement data to the physician. The corresponding software is executed during the patient conducting the eye (vision) exam and enables the remotely located physician, to participate in the same via video conference. Alternatively, an artificial intelligence resource operating remotely or locally can provide physician-type guidance and eye health assessments. Alignment of device and patient eyes can be facilitated for remote exams.

A fundus imaging apparatus includes a scanning optical system, a control circuit, and an image forming unit. The scanning optical system scans a fundus of a subject's eye with light from a light source, and receives return light from the fundus by a light receiver. The control circuit controls the scanning optical system such that a scanning locus is formed by the light in the fundus. The image forming unit forms an image of the fundus based on a light receiving signal from the light receiver and a position of the scanning locus. The control circuit is capable of performing an alignment mode, in which the control circuit controls the scanning optical system to project an alignment indicator for aligning the scanning optical system with the subject's eye on the fundus based on the light from the light source.

The methods and systems provided can automatically determine an Arteriolar-to-Venular diameter Ratio, AVR, in blood vessels, such as retinal blood vessels and other blood vessels in vertebrates. The AVR is an important predictor of increases in the risk for stroke, cerebral atrophy, cognitive decline, and myocardial infarct.

A refraction device determines a refraction end point to provide corrective optics for a test subject. The device includes an adjustable optical system providing corrective optics to the test subject and an adjustable viewing target disposed along an optical path such as to be viewable through the adjustable optical system by a test subject. The adjustable viewing target includes a directional indicator linked synchronously to at least two choices of corrective optics presented to the test subject.

A fundus imaging apparatus includes a scanning optical system, a control circuit, and an image forming unit. The scanning optical system scans a fundus of a subject's eye with light from a light source, and receives return light from the fundus by a light receiver. The control circuit controls the scanning optical system such that a scanning locus is formed by the light in the fundus. The image forming unit forms an image of the fundus based on a light receiving signal from the light receiver and a position of the scanning locus. The control circuit is capable of performing an alignment mode, in which the control circuit controls the scanning optical system to project an alignment indicator for aligning the scanning optical system with the subject's eye on the fundus based on the light from the light source.

Systems and methods are provided for fingertip control of an ophthalmic surgical instrument during ophthalmic surgery. A hand control unit worn by the ophthalmic surgeon comprises at least one fingertip cover, with at least one sensor in the fingertip cover. The sensor may detect the surgeon's selection of the ophthalmic surgical instrument, the amount of finger pressure applied by the surgeon, and other parameters, allowing the surgeon to use fingertip action to control a function of the ophthalmic surgical instrument.

The methods and systems provided can automatically determine an Arteriolar-to-Venular diameter Ratio, AVR, in blood vessels, such as retinal blood vessels and other blood vessels in vertebrates. The AVR is an important predictor of increases in the risk for stroke, cerebral atrophy, cognitive decline, and myocardial infarct.

An artificial intelligence eye disease screening and diagnostic system based on an ophthalmic robot, comprising a human eye positioning analysis module, an image information collection module, an AI picture quality monitoring module, an eye disease analysis and diagnosis module, a data storage management module, and an execution control module. The system can replace ophthalmologists to perform eye disease diagnosis tasks in regions lacking ophthalmologists, and assists the ophthalmologists in eye disease screening and diagnosis in large hospitals with a large number of patients, thus improving diagnosis efficiency.

Various embodiments of an eye imaging apparatus are disclosed. In some embodiments, the eye imaging apparatus may comprise a light source, an image sensor, a hand-held computing device, and an adaptation module. The adaptation module comprises a microcontroller and a signal processing unit configured to adapt the hand-held computing device to control the light source and the image sensor. In some embodiments, the imaging apparatus may comprise an exterior imaging module to image an anterior segment of the eye and/or a front imaging module to image a posterior segment of the eye. The eye imaging apparatus may be used in an eye imaging medical system. The images of the eye may be captured by the eye imaging apparatus, transferred to an image computing module, stored in an image storage module, and displayed in an image review module.

The present application includes methods, systems and computer readable storage devices for determining a color score for at least a portion of a biological tissue. The subject matter of the application is embodied in a method that includes obtaining a digital image of the biological tissue, and receiving a selection of a portion of the image as an evaluation area. The method also includes determining for each of a plurality of pixels within the evaluation area, a plurality of color components that are based on a Cartesian color space, and determining, from the color components, a hue value in a polar coordinate based color space. The method further includes determining a color value based on the hue value for each of the plurality of pixels, and assigning a color score to the evaluation area based on an average of the color values of the plurality of pixels.