Abstract: Embodiments disclosed herein generally relate to predicting geographic-atrophy lesion growth and/or geographic atrophy lesion size in an eye. The predictions can be generated by processing a data object using a neural network. The data object may include a three-dimensional data object representing a depiction of at least part of the eye or a multi-channel data object representing one or more decorresponding pictions of at least part of the eye. The neural network can include a convolutional multi-task neural network that is trained to learn features that are predictive of both lesion-growth and lesion-size outputs.

Abstract: Systems and methods disclosed herein relate to using a machine-learning model to process an input of a subject's eye and to predict a current or future visual acuity of the subject. The subject may have been diagnosed with age-related macular degeneration. The predicted current or future visual acuity may be used to (for example) facilitate diagnosing the subject (e.g., with a particular type of age-related macular degeneration), facilitate identifying a treatment strategy for the subject, and/or facilitate designing a clinical study.