公开(公告)号：US20230260126A1

公开(公告)日：2023-08-17

申请号：US18305789

申请日：2023-04-24

Applicant: Google LLC

Inventor： Lily Hao Yi Peng ,  Dale R. Webster ,  Philip Charles Nelson ,  Varun Gulshan ,  Marc Adlai Coram ,  Martin Christian Stumpe ,  Derek Janme Wu ,  Arunachalam Narayanaswamy ,  Avinash Vaidyanathan Varadarajan ,  Katharine Blumer ,  Yun Liu ,  Ryan Poplin

IPC: G06T7/00 ,  G16H50/30 ,  G16H50/20 ,  G06N3/044 ,  G06N3/08

CPC classification number: G06T7/0016 ,  G16H50/30 ,  G16H50/20 ,  G06N3/044 ,  G06N3/08 ,  G01N2800/168 ,  G01N2800/164 ,  G06T2207/10016 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/30041

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models. One of the methods includes obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data.

公开(公告)号：US20230169652A1

公开(公告)日：2023-06-01

申请号：US18011888

申请日：2022-05-12

Applicant: Google LLC

Inventor： Sahar Kazemzadeh ,  Dong Jin Yu ,  Shahar Jamshy ,  Rory Pilgrim ,  Zaid Isam Nabulsi ,  Andrew Beckmann Sellergren ,  Yun Liu ,  Shruthi Prabhakara ,  Atilla Peter Kiraly

IPC: G06T7/00 ,  G06N3/0464 ,  G06N3/084 ,  G16H10/60 ,  G16H30/40 ,  G16H50/20 ,  G16H50/30 ,  G06T7/11 ,  A61B6/00

CPC classification number: G06T7/0012 ,  G06N3/0464 ,  G06N3/084 ,  G16H10/60 ,  G16H30/40 ,  G16H50/20 ,  G16H50/30 ,  G06T7/11 ,  A61B6/50 ,  G06T2207/10116 ,  G06T2207/30061 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems and methods for chest condition determination can leverage one or more machine-learned models to process radiograph data to determine risk data (e.g., a preliminary diagnosis). For example, systems and methods can utilize a pathology model to process a chest x-ray to generate a tuberculosis diagnosis. The one or more machine-learned models can segment the lungs, can detect features in the data, and can pool the segmentation and located features to determine the diagnosis.

公开(公告)号：US20220359062A1

公开(公告)日：2022-11-10

申请号：US17620445

申请日：2020-09-11

Applicant: Google LLC

Inventor： Robert Carter Dunn ,  Ayush Jain ,  Peggy Yen Phuong Bui ,  Clara Eng ,  David Henry Way ,  Kang Li ,  Vishakha Gupta ,  Jessica Gallegos ,  Dennis Ai ,  Yun Liu ,  David Coz ,  Yuan Liu

IPC: G16H30/40 ,  G16H50/20 ,  G16H10/60

Abstract: The present disclosure is directed to a deep learning system for differential diagnoses of skin diseases. In particular, the system performs a method that can include obtaining a plurality of images that respectively depict a portion of a patient's skin. The method can include determining, using a machine-learned skin condition classification model, a plurality of embeddings respectively for the plurality of images. The method can include combining the plurality of embeddings to obtain a unified representation associated with the portion of the patient's skin. The method can include determining, using the machine-learned skin condition classification model, a skin condition classification for the portion of the patients skin, the skin condition classification produced by the machine-learned skin condition classification model by processing the unified representation, wherein the skin condition classification identifies one or more skin conditions selected from a plurality of potential skin conditions.