公开(公告)号：US11361440B2

公开(公告)日：2022-06-14

申请号：US17317989

申请日：2021-05-12

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Xin Wang ,  Youbing Yin ,  Bin Kong ,  Yi Lu ,  Junjie Bai ,  Zhenghan Fang ,  Qi Song

IPC: G06T7/00 ,  A61B6/00 ,  A61B5/00 ,  A61B6/03 ,  G16H50/20 ,  G16H10/60 ,  G06N3/04 ,  G06N3/08 ,  G16H30/40 ,  A61B6/02

Abstract: Embodiments of the disclosure provide methods and systems for disease condition prediction from images of a patient. The system may include a communication interface configured to receive a sequence of images acquired of the patient by an image acquisition device. The sequence of images are acquired at a sequence of prior time points during progression of a disease. The system may include a processor, configured to determine regions of interest based on the sequence of images. The processor applies a progressive condition prediction network to the regions of interest to predict a level of disease progression at a future time point during the progression of the disease. The progressive condition prediction network predicts the level of disease progression based on the regions of interest and disease conditions at the sequence of prior time points. The processor further provides a diagnostic output based on the predicted level of disease progression.

公开(公告)号：US20210374950A1

公开(公告)日：2021-12-02

申请号：US17121595

申请日：2020-12-14

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Feng Gao ,  Zhenghan Fang ,  Yue Pan ,  Junjie Bai ,  Youbing Yin ,  Hao-Yu Yang ,  Kunlin Cao ,  Qi Song

IPC: G06T7/00 ,  G06K9/62 ,  G06T3/40 ,  G06T7/70 ,  A61B6/03 ,  A61B6/00

Abstract: The disclosure relates to systems and methods for vessel image analysis. The method includes receiving a set of images along a vessel acquired by a medical imaging device, and determining a sequence of centerline points along the vessel and a sequence of image patches at the respective centerline points based on the set of images. The method further includes detecting plaques based on the sequence of image patches using a first learning network. The first learning network includes an encoder configured to extract feature maps based on the sequence of image patches and a plaque range generator configured to generate a start position and an end position of each plaque based on the extracted feature maps. The method also includes classifying each detected plaque and determining a stenosis degree for the detected plaque, using a second learning network reusing at least part of the parameters of the first learning network and the extracted feature maps.

公开(公告)号：US11076824B1

公开(公告)日：2021-08-03

申请号：US17067181

申请日：2020-10-09

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Xin Wang ,  Youbing Yin ,  Bin Kong ,  Yi Lu ,  Junjie Bai ,  Zhenghan Fang ,  Qi Song

IPC: A61B6/00 ,  G06T7/00 ,  A61B5/00 ,  A61B6/03 ,  G16H50/20 ,  G16H10/60 ,  G06N3/04 ,  G06N3/08 ,  G16H30/40 ,  A61B6/02

Abstract: Embodiments of the disclosure provide methods and systems for detecting COVID-19 from a lung image of a patient. The exemplary system may include a communication interface configured to receive the lung image acquired of the patient's lung by an image acquisition device. The system may further include at least one processor, configured to determine a region of interest comprising the lung from the lung image and apply a COVID-19 detection network to the region of interest to determine a condition of the lung. The COVID-19 detection network is a multi-class classification learning network that labels the region of interest as one of COVID-19, non-COVID-19 pneumonia, non-pneumonia abnormal, or normal. The at least one processor is further configured to provide a diagnostic output based on the determined condition of the lung.

公开(公告)号：US20220301156A1

公开(公告)日：2022-09-22

申请号：US17591758

申请日：2022-02-03

Applicant: Shenzhen Keya Medical Technology Corporation,

Inventor： Zhenghan Fang ,  Junjie Bai ,  Youbing Yin ,  Xinyu Guo ,  Qi Song

IPC: G06T7/00 ,  G16H30/40

Abstract: Embodiments of the disclosure provide systems and methods for analyzing medical images using a learning model. The system receives a medical image acquired by an image acquisition device. The system may additionally include at least one processor configured to apply the learning model to perform an image analysis task on the medical image. The learning model is trained jointly with an error estimator using training images comprising a first set of labeled images and a second set of unlabeled images. The error estimator is configured to estimate an error of the learning model associated with performing the image analysis task.

公开(公告)号：US11357464B2

公开(公告)日：2022-06-14

申请号：US17317487

申请日：2021-05-11

Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION

Inventor： Xin Wang ,  Youbing Yin ,  Bin Kong ,  Yi Lu ,  Junjie Bai ,  Zhenghan Fang ,  Qi Song

IPC: G06T7/00 ,  A61B6/03 ,  G16H50/20 ,  A61B6/00 ,  A61B5/00 ,  G16H10/60 ,  G06N3/04 ,  G06N3/08 ,  G16H30/40 ,  A61B6/02

Abstract: Embodiments of the disclosure provide methods and systems for determining a disease condition from a 3D image of a patient. The exemplary system may include a communication interface configured to receive the 3D image acquired of the patient by an image acquisition device. The system may further include a processor, configured to determine a 3D region of interest from the 3D image and apply a detection network to the 3D region of interest to determine the disease condition and a severity of the disease condition. The detection network is a multi-task learning network that determines the disease condition based on one or more lesion masks determined from the 3D region of interest and determines the severity of the disease condition from the 3D region of interest. The processor is further configured to provide a diagnostic output based on the disease condition and the severity of the disease condition.