Abstract: A method and system for managing a treatment of a subject diagnosed with a macular edema condition. Subject data for a subject is received. The subject data comprises best corrected visual acuity (BCVA) data for the subject. An input for a computational model is generated using the subject data. An injection frequency for the treatment of the subject diagnosed with the macular edema condition is predicted, via the computational model, based on the input.

Abstract: Methods and systems for evaluating diabetic retinopathy (DR) severity are provided herein. Color fundus imaging data is received for an eye being evaluated for DR. A metric is generated using the color fundus imaging data, the metric indicating a probability that a score for the DR severity in the eye falls within a selected range.

Abstract: Methods and systems for evaluating diabetic retinopathy (DR) severity are provided herein. Color fundus imaging data is received for an eye being evaluated for DR. A metric is generated using the color fundus imaging data, the metric indicating a probability that a score for the DR severity in the eye falls within a selected range.

Abstract: A method for identifying a type of choroidal neovascularization in a retina of a human eye is disclosed, the method comprising receiving, in a processor, optical coherence tomography data of the eye; generating, in the processor, volume segments of the eye using the optical coherence tomography data and a neural network; and identifying, in the processor, the type of choroidal neovascularization in the eye, using the volume segments, the type of choroidal neovascularization comprising one or more of the following: classic choroidal neovascularization and occult choroidal neovascularization.

Abstract: Methods and systems disclosed herein relate generally to systems and methods for predicting a future visual acuity of a subject by using machine-learning models. An image of at least part of a retina of a subject can be processed by one or more first machine-learning models to detect a set of retina-related segments. Segment-specific metrics that characterize a retina-related segment of the set of retina-related segments can be generated. The segment-specific metrics can be processed by using a second machine-learning model to generate a result corresponding to a prediction corresponding to a future visual acuity of the subject.