公开(公告)号：US20240312476A1

公开(公告)日：2024-09-19

申请号：US18382228

申请日：2023-10-20

Applicant: TELADOC HEALTH, INC.

Inventor： John O'Donovan ,  Pushkar Shukla ,  Paul C. McElroy ,  Sushil Bharati ,  Marco Pinter

IPC: G10L25/66 ,  A61B5/00 ,  A61B5/11 ,  G06N3/044 ,  G06N3/08 ,  G06V10/764 ,  G06V20/40 ,  G06V40/16 ,  G06V40/20 ,  G10L15/22 ,  G10L15/26 ,  G16H10/60 ,  G16H15/00 ,  G16H40/67 ,  G16H50/20 ,  G16H50/30

CPC classification number: G10L25/66 ,  A61B5/1124 ,  A61B5/4064 ,  A61B5/4803 ,  A61B5/7267 ,  A61B5/7282 ,  G06N3/08 ,  G06V10/764 ,  G06V20/40 ,  G06V40/168 ,  G06V40/28 ,  G10L15/22 ,  G10L15/26 ,  G16H15/00 ,  G16H40/67 ,  G16H50/20 ,  G16H50/30 ,  G06N3/044 ,  G06V2201/03 ,  G16H10/60

Abstract: A system for automated health condition scoring includes at least one communication interface to receive an audio stream and a video stream from an endpoint in proximity to a patient, at least two different artificial intelligence (“AI”) detectors to respectively process one or both of the audio stream and the video stream using machine learning to automatically determine at least two respective likelihoods of the patient having a health condition, an AI scorer to combine the at least two respective likelihoods of the health condition using machine learning to automatically determine a health condition score representing an overall likelihood of the patient having the health condition, and a display interface that displays an indication of the health condition score to a physician.

公开(公告)号：US20240312229A1

公开(公告)日：2024-09-19

申请号：US18280270

申请日：2020-12-21

Applicant: GUANGZHOU KINGMED CENTER FOR CLINICAL LABORATORY ,  GUANGZHOU KINGMED DIAGNOSTICS GROUP CO., LTD. ,  GUANGZHOU KINGMED TRANSLATIONAL MEDICINE INSTITUTE CO., LTD.

Inventor： Shuanlong CHE ,  Tingsong YU ,  Si LIU ,  Fang LU ,  Kangpei TAO ,  Xin LI ,  Pifu LUO ,  Yinghua LI ,  Weisong QIU

IPC: G06V20/69 ,  G06V30/14 ,  G16H70/60

CPC classification number: G06V20/695 ,  G06V20/698 ,  G06V30/1444 ,  G16H70/60 ,  G06V2201/03

Abstract: A method for identifying a target region of a digital pathology slide, including: obtaining a scanned image of a pathology slide; inputting the scanned image of the pathology slide into a preset deep learning-based identification model; extracting a contour feature of the scanned image of the pathology slide by using an image contour feature extraction submodel, to obtain a contour image; segmenting the contour image by using an image segmentation submodel to obtain a plurality of sub-contour images; separately performing classification and identification on the plurality of sub-contour images by using an image classification submodel, to obtain a region category corresponding to each sub-contour image; and determining a target region image based on the region category of each sub-contour image. In addition, a system for identifying a target region of a digital pathology slide, a device, and a medium are further proposed.

公开(公告)号：US12094117B2

公开(公告)日：2024-09-17

申请号：US18487438

申请日：2023-10-16

Applicant: ADIUVO DIAGNOSTICS PRIVATE LIMITED

Inventor： Bala Pesala ,  Geethanjali Radhakrishnan ,  Bikki Kumar Sha ,  John King

IPC: G06T7/00 ,  G01N21/64 ,  G06T7/50 ,  G06V10/10 ,  G06V10/60 ,  G06V10/82

CPC classification number: G06T7/0012 ,  G06T7/50 ,  G06V10/17 ,  G06V10/60 ,  G06V10/82 ,  G01N21/6486 ,  G06T2207/10064 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T2207/30088 ,  G06V2201/03 ,  G06V2201/07

Abstract: Techniques are for detecting presence of a problematic cellular entity in a target. In an example, using an analysis model, a fluorescence-based image is analyzed. The analysis model is trained using a number of reference fluorescence-based images for detecting the presence of problematic cellular entities in targets. Based on the analysis, a problematic cellular entity present in the target is detected. To perform the detection, the analysis model is trained to differentiate between the fluorescence in the fluorescence-based image emerging from the problematic cellular entity and the fluorescence in the fluorescence-based image emerging from regions other than the problematic cellular entity.

公开(公告)号：US12092569B2

公开(公告)日：2024-09-17

申请号：US17506762

申请日：2021-10-21

Applicant: Green Vision Systems Ltd.

Inventor： Danny S. Moshe

IPC: G01N21/31 ,  G01N1/44 ,  G01N33/497 ,  G06K9/00 ,  G06K9/46 ,  G06V10/58 ,  G06V10/60 ,  G06V20/69 ,  A61B5/097

CPC classification number: G01N21/31 ,  G01N1/44 ,  G01N33/497 ,  G06V10/58 ,  G06V10/60 ,  G06V20/693 ,  A61B5/097 ,  G06V2201/03

Abstract: A method of detecting a biological substance in a sample, comprises: illuminate the sample by light; imaging the illuminated sample by Fourier transform hyperspectral imaging; and analyzing the obtained hyperspectral image to detect the biological substance in a sample.

公开(公告)号：US12079311B2

公开(公告)日：2024-09-03

申请号：US17145123

申请日：2021-01-08

Applicant: Salesforce, Inc.

Inventor： Carlos Andres Esteva ,  Douwe Stefan van der Wal

IPC: G06T7/11 ,  G06F18/214 ,  G06F18/2413 ,  G06F18/40 ,  G06N3/08 ,  G06T7/00 ,  G06V10/25 ,  G06V10/40

CPC classification number: G06F18/2413 ,  G06F18/214 ,  G06F18/40 ,  G06N3/08 ,  G06T7/0012 ,  G06T7/11 ,  G06V10/25 ,  G06V10/40 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06V2201/03

Abstract: An AI-enhanced data labeling tool assists a human operator in annotating image data. The tool may use a segmentation model to identify portions to be labeled. Initially, the operator manually annotates portions and once the operator has labeled a sufficient number of portions, a classifier is trained to predict labels for other portions. The predictions generated by the classifier are presented to the operator for approval or modification. The tool may also include an active learning model that recommends portions of the image data for the operator to annotate next. The active learning model may suggest one or more batches of portions based on the extent to which, once labeled, those batches will increase the diversity of the total set of labeled portions.

公开(公告)号：US20240290085A1

公开(公告)日：2024-08-29

申请号：US18572506

申请日：2022-06-24

Applicant: BIO-RAD EUROPE GMBH

Inventor： Thomas Picard

IPC: G06V10/98 ,  G01N21/88 ,  G06V10/75 ,  G06V10/764 ,  G06V10/776 ,  G06V20/69

CPC classification number: G06V10/993 ,  G01N21/8851 ,  G06V10/75 ,  G06V10/764 ,  G06V10/776 ,  G01N2021/8887 ,  G06V20/698 ,  G06V2201/03

Abstract: An input image is received from testing equipment. One or more synthetic images are generated by applying an image-to-image translation model to the input image. Based on the one or more synthetic images. a binary classifier is applied to determine a classification for the received input image.

公开(公告)号：US20240274287A1

公开(公告)日：2024-08-15

申请号：US18433422

申请日：2024-02-06

Applicant: FUJIFILM Corporation

Inventor： Aya OGASAWARA

IPC: G16H50/20 ,  A61B5/00 ,  G06V20/70 ,  G16H10/60 ,  G16H15/00 ,  G16H40/67

CPC classification number: G16H50/20 ,  A61B5/4848 ,  A61B5/742 ,  G06V20/70 ,  G16H10/60 ,  G16H15/00 ,  G16H40/67 ,  G06V2201/03

Abstract: Provided are an image diagnosis assisting device, an operation method of an image diagnosis assisting device, and a program that can ascertain a treatment history using information regarding a past treatment location obtained from a medical image, an interpretation report, and the like. An image diagnosis assisting device acquires a first medical image generated by imaging a subject in a latest examination, acquires treatment information regarding a treatment of the subject generates treatment progress information regarding a past treatment location in the subject based on the treatment information, and performs control to display the treatment progress information on a screen on which the first medical image is displayed.

公开(公告)号：US20240273727A1

公开(公告)日：2024-08-15

申请号：US18409956

申请日：2024-01-11

Applicant: Siemens Healthineers AG

Inventor： Long Xie ,  Eli Gibson

IPC: G06T7/00 ,  G06T7/30 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G16H30/40

CPC classification number: G06T7/0016 ,  G06T7/30 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G16H30/40 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06V2201/03

Abstract: Image-based biomarkers are provided for active disease progression of Alzheimer's disease and related dementias (ADRD) by training and using artificial neural networks (ANNs) on the basis of subject's images. A cross-sectional sub-pipeline and a longitudinal sub-pipeline are used for processing different images parts, namely cross-sectional imaging data and longitudinal imaging data. A patch-based multiple instance learning (MIL) scheme is applied.

公开(公告)号：US20240265667A1

公开(公告)日：2024-08-08

申请号：US18559465

申请日：2022-05-12

Applicant: Augmedics, Inc.

Inventor： Cristian J. Luciano ,  Krzysztof B. Siemionow ,  Dominik Gawel ,  Edwing Isaac Mejía Orozco ,  Milo Jankovic

IPC: G06V10/26 ,  G06V10/30 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V20/70

CPC classification number: G06V10/26 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V20/70 ,  G06V10/30 ,  G06V2201/03

Abstract: Systems, devices, and methods for segmentation of patient anatomy are described herein. A method can include receiving a three-dimensional (3D) scan volume including a set of images of a 3D region of patient anatomy. The 3D region of patient anatomy can include a set of anatomical structures. The method can also include generating a set of two-dimensional (2D) radiographs using the 3D scan volume. Each 2D radiograph from the set of 2D radiographs can include 3D image data extracted from the 3D scan volume. The method can also include training a segmentation model to segment 2D radiographs using the set of 2D radiographs to identify one or more anatomical parts of interest.

公开(公告)号：US12051200B2

公开(公告)日：2024-07-30

申请号：US17606400

申请日：2020-04-24

Applicant: NANJING TURING MICROBIAL TECHNOLOGIES CO. LTD

Inventor： Zhongxiao Wang ,  Wei Wu

IPC: G06T7/00 ,  G06V10/26 ,  G06V10/70 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G06V10/94 ,  G06V20/69 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G06V10/26 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G06V10/87 ,  G06V10/95 ,  G06V10/955 ,  G06V20/695 ,  G06V20/698 ,  G16H50/20 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06V2201/03

Abstract: An artificial intelligence (AI)-based medical image automatic diagnosis system and method. The method comprises: acquiring a medical microscope image and corresponding diagnostic data; annotating the medical microscope images to obtain annotation data corresponding to the medical microscope images; building a training set and a test set on the basis of the diagnostic data and annotation data corresponding to the medical microscope images; and performing training on the basis of a deep learning model to obtain the optimal AI classification model and the optimal AI semantic segmentation model to implement the automatic diagnosis of the medical microscope image of a test sample. The system and method can effectively save human resources, shorten the diagnosis time, and improve diagnosis accuracy.