Hoffmann-La Roche Inc.

F. Hoffmann-La Roche Ag

Fethallah Benmansour

Andreas Maunz

Yun Li

Thomas Felix Albrecht

Andreas Thalhammer

Yan-Ping Zhang Schaerer

Jayashree Sahni

Filippo Arcadu

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.

Machine-learning techniques for prediction of future visual acuity

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.

Choroidal neovascularization classification

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Basel, Switzerland

Fethallah Benmansour