公开(公告)号：US20230326024A1

公开(公告)日：2023-10-12

申请号：US18328305

申请日：2023-06-02

Applicant: Genentech, Inc.

Inventor： Qi YANG ,  Neha Sutheekshna ANEGONDI ,  Simon Shang GAO

IPC: G06T7/00

CPC classification number: G06T7/0012 ,  G06T2207/30096 ,  G06T2207/30041 ,  G06T2207/10048 ,  G06T2207/10101 ,  G06T2207/10064

Abstract: A method and system for evaluating geographic atrophy in a retina. A set of fundus autofluorescence (FAF) images of the retina is received at a machine learning system. A set of optical coherence tomography (OCT) images of the retina is received at the machine learning system. A lesion growth rate is predicted, via the machine learning system, for a geographic atrophy lesion in the retina using the set of FAF images and the set of OCT images.

公开(公告)号：US20230036463A1

公开(公告)日：2023-02-02

申请号：US17782476

申请日：2020-12-04

Applicant: Genentech, Inc.

Inventor： Qi YANG ,  Simon S. GAO

IPC: A61B5/00 ,  G06T7/12 ,  G06T7/60 ,  G06T7/00 ,  A61B3/10 ,  A61B3/00

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.

公开(公告)号：US20230309919A1

公开(公告)日：2023-10-05

申请号：US18328264

申请日：2023-06-02

Applicant: Genentech, Inc. ,  Hoffmann-La Roche Inc.

Inventor： Qi YANG ,  Fethallah BENMANSOUR ,  Daniela Ferrara CAVALCANTI ,  Dimitrios DAMOPOULOS

IPC: A61B5/00 ,  G06T7/11 ,  G06T7/194 ,  G06T5/00 ,  G06T7/00

CPC classification number: A61B5/4842 ,  G06T7/11 ,  G06T7/194 ,  G06T5/009 ,  G06T7/0012 ,  G06T2207/10024 ,  G06T2207/30041 ,  G06T2207/20084 ,  G06T2207/20076

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.