The present disclosure provides a system for predicting diabetic retinopathy progression. The system includes an image-capturing module and a processing unit. The image-capturing module is configured to capture a first fundus image of a user at a first time and a second fundus image of the user at a second time different from the first time. The processing unit is configured to receive the first fundus image and the second fundus image, compare the first fundus image and the second fundus image and indicate a difference between the first fundus image and the second fundus image. The processing unit is also configured to provide a prediction in a diabetic retinopathy progression of the user based on the difference. A method for predicting diabetic retinopathy progression is also provided in the present disclosure.

Methods for automated segmentation system for retinal blood vessels from optical coherence tomography angiography images include a preprocessing stage, an initial segmentation stage, and a refining stage. Application of machine-learning techniques to segmented images allow for automated diagnosis of retinovascular diseases, such as diabetic retinopathy.

A subjective optometry apparatus includes an optometry unit having an optical member, being located in front of a subject eye, and changing optical characteristics of a target light flus with using the optical member, and a measurement optical system that has a light projecting optical system for applying measurement light emitted from a measurement light source to a fundus of the subject eye through the optometry unit, and a light receiving optical system in which a detector receives reflected light of the measurement light reflected on the fundus of the subject eye through the optometry unit, and that objectively measures the optical characteristics of the subject eye. An optical axis of the measurement optical system is set to be off-axis from an optical axis of the optical member in the optometry unit.

A subjective optometry apparatus includes an optometry unit having an optical member, being located in front of a subject eye, and changing optical characteristics of a target light flus with using the optical member, and a measurement optical system that has a light projecting optical system for applying measurement light emitted from a measurement light source to a fundus of the subject eye through the optometry unit, and a light receiving optical system in which a detector receives reflected light of the measurement light reflected on the fundus of the subject eye through the optometry unit, and that objectively measures the optical characteristics of the subject eye. An optical axis of the measurement optical system is set to be off-axis from an optical axis of the optical member in the optometry unit.

System for customizing visual field (VF) tests uses a machine learning model (15) trained on retina images (12A, 12C, 12D), including optical coherence tomography (OCT), optical coherence tomography angiography (OCTA), fundus, and/or fluorescein angiography images. In operation, in preparation for administering a specific VF test (13) to a patient, a retina image of the patient is submitted to the present machine model, which responds by synthesizing a VF prediction for the patient. The synthesized VF may be used to optimize the specific VF test prior to administering it to the patient.

System for customizing visual field (VF) tests uses a machine learning model (15) trained on retina images (12A, 12C, 12D), including optical coherence tomography (OCT), optical coherence tomography angiography (OCTA), fundus, and/or fluorescein angiography images. In operation, in preparation for administering a specific VF test (13) to a patient, a retina image of the patient is submitted to the present machine model, which responds by synthesizing a VF prediction for the patient. The synthesized VF may be used to optimize the specific VF test prior to administering it to the patient.

An ophthalmic apparatus includes an illumination optical system, an optical scanner, an imaging optical system, a controller, and an image forming unit. The illumination optical system is configured to generate slit-shaped illumination light. The optical scanner is configured to deflect the illumination light to guide the illumination light to a fundus of a subject's eye. The imaging optical system is configured to guide returning light of the illumination light from the fundus to an image sensor. The controller is configured to control the optical scanner. The image forming unit is configured to form an image of the fundus based on a light receiving result captured in an imaging target region on a light receiving surface of the image sensor. The image sensor is configured to capture the light receiving result in an opening region on the light receiving surface using a rolling shutter method, the opening region corresponding to an illumination region of the illumination light on the fundus, the illumination region being moved in a predetermined scan direction by the optical scanner. The controller is configured to control the optical scanner so that irradiation times of the returning light at a plurality of light receiving elements in the imaging target region are substantially equal.

An ophthalmic apparatus includes an illumination optical system, an optical scanner, an imaging optical system, a controller, and an image forming unit. The illumination optical system is configured to generate slit-shaped illumination light. The optical scanner is configured to deflect the illumination light to guide the illumination light to a fundus of a subject's eye. The imaging optical system is configured to guide returning light of the illumination light from the fundus to an image sensor. The controller is configured to control the optical scanner. The image forming unit is configured to form an image of the fundus based on a light receiving result captured in an imaging target region on a light receiving surface of the image sensor. The image sensor is configured to capture the light receiving result in an opening region on the light receiving surface using a rolling shutter method, the opening region corresponding to an illumination region of the illumination light on the fundus, the illumination region being moved in a predetermined scan direction by the optical scanner. The controller is configured to control the optical scanner so that irradiation times of the returning light at a plurality of light receiving elements in the imaging target region are substantially equal.

A medical diagnostic system can assess quality of a representation of a body part determined based on a response of the body part to exposure to electromagnetic waves, process the representation with a disease detection machine learning model to determine a certainty measure for a presence of a disease, determine a quality score for the representation based on the quality of the representation and the certainty measure, and discard the at least one representation based on the quality score. Combining machine learning in conjunction with one another, such as, for quality assessment and disease detection, can provide for more accurate image quality analysis, lead to faster medical imaging, and reduce the need to retake images or entirely re-perform medical imaging. The system can be easier to use, be more robust and faster than other systems by reducing the need to retake images while maintaining performance of the system.

A method for determining a calibration factor in Doppler flowmetry velocity measurements in the living eye includes imaging the eye with Doppler flowmetry and processing data to obtain blood velocity, volume, and flow maps using Doppler flowmetry formulas that provide velocity as a mean frequency expressed in Hz, and volume and flow in arbitrary units. A selected blood vessel is probed with Doppler OCT to measure the absolute velocity of blood at that location expressed in mm/s to determine a calibration factor used to convert the velocity measured with Doppler flowmetry expressed in Hz to velocity expressed in mm/s.