公开(公告)号：US20240087120A1

公开(公告)日：2024-03-14

申请号：US18513106

申请日：2023-11-17

Applicant: GENENTECH, INC.

Inventor： Neha Sutheekshna ANEGONDI ,  Simon Shang GAO ,  Julia Gabriella CLUCERU

IPC: G06T7/00 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G16H50/20 ,  G06T2207/20081

Abstract: A method for evaluating geographic atrophy. A set of retinal images is received. Each model of a plurality of models is trained to predict a set of geographic atrophy (GA) progression parameters for a geographic atrophy (GA) lesion using the set of retinal images. A visualization output is generated for each model of the plurality of models. The visualization output for a corresponding model of the plurality of models provides information about how the corresponding model uses the set of retinal images to predict the set of GA progression parameters.

公开(公告)号：US20230154595A1

公开(公告)日：2023-05-18

申请号：US18153762

申请日：2023-01-12

Applicant: Genentech, Inc.

Inventor： Simon Shang GAO ,  Neha Sutheekshna ANEGONDI

IPC: G16H30/40 ,  G16H50/20

CPC classification number: G16H30/40 ,  G16H50/20

Abstract: A method and system for evaluating geographic atrophy in a retina. A set of fundus autofluorescence (FAF) images of the retina is received. An input is generated for a machine learning system using the set of fundus autofluorescence images. A lesion area is predicted, via the machine learning system, for the geographic atrophy lesion in the retina using the set of fundus autofluorescence images. A lesion growth rate is predicted, via the machine learning system, for the geographic atrophy lesion in the retina using the input.

公开(公告)号：US20240038370A1

公开(公告)日：2024-02-01

申请号：US18482237

申请日：2023-10-06

Applicant: Genentech, Inc.

Inventor： Neha Sutheekshna ANEGONDI ,  Jian DAI ,  Michael Gregg KAWCZYNSKI ,  Yusuke Alexander KIKUCHI

IPC: G16H30/40 ,  G16H10/60 ,  G06T7/00

CPC classification number: G16H30/40 ,  G16H10/60 ,  G06T7/0012 ,  G06T2207/30041 ,  G06T2207/10101 ,  G06T2207/20081

Abstract: A method and system for predicting a treatment outcome. Three-dimensional imaging data for a retina of a subject is received. A first output is generated using a deep learning system and the three-dimensional imaging data. The first output and baseline data are received as input for a symbolic model. A treatment outcome is predicted, via the symbolic model, for the subject undergoing a treatment for neovascular age-related macular degeneration (nAMD) using the input.

公开(公告)号：US20230342935A1

公开(公告)日：2023-10-26

申请号：US18304006

申请日：2023-04-20

Applicant: Genentech, Inc.

Inventor： Neha Sutheekshna ANEGONDI ,  Simon Shang GAO ,  Jiaxiang JIANG ,  Michael Gregg KAWCZYNSKI ,  Jasmine PATIL ,  Theodore C. SPAIDE

IPC: G06T7/00 ,  G06T7/174 ,  G06T7/62 ,  A61B3/10 ,  A61B3/12

CPC classification number: G06T7/0014 ,  G06T7/174 ,  G06T7/62 ,  A61B3/102 ,  A61B3/1225 ,  G06T2207/10064 ,  G06T2207/10048 ,  G06T2207/10101 ,  G06T2207/30041 ,  G06T2207/30096 ,  G06T2207/20084 ,  G06T2207/20021

Abstract: A method and system for generating a geographic atrophy (GA) lesion segmentation mask corresponding to GA lesions in a retina is disclosed herein. In some embodiments, a set of fundus autofluorescence (FAF) images of a retina having one or more geographic atrophy (GA) lesions and one or both of a set of infrared (IR) images of the retina or a set of optical coherence tomography (OCT) images of the retina may be used to generate the GA lesion segmentation mask including one or more GA lesion segments corresponding to the one or more GA lesions in the retina. In some instances, a neural network may be used to generate the GA lesion segmentation mask.

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.