-
公开(公告)号：US20230098121A1
公开(公告)日：2023-03-30
申请号：US17489682
申请日：2021-09-29
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Feng Gao , Hao-Yu Yang , Yue Pan , Youbing Yin , Qi Song
IPC: G16H50/20 , G16H10/60 , G16H50/30 , G16H50/50 , G16H30/40 , G06F16/23 , A61B5/00 , A61B5/02 , A61B5/107
Abstract: The disclosure relates to a method for prognosis management based on medical information of a patient, a device, and a medium. The method includes acquiring, by a processor, medical information of the patient at a first time. The method further includes receiving the medical information of the patient at a first time. The method may further include predicting, by a processor, a progression condition of an object associated with the patient at a second time based on the acquired medical information of the first time. The progression condition is indicative of a prognosis risk, and the second time is after the first time. The method may also include outputting the predicted progression condition to an information management system. The method is helpful for users to understand the potential prognosis risk of the object at the second time to aid users in making treatment decisions.
-
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
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：US20220284571A1
公开(公告)日：2022-09-08
申请号：US17505917
申请日：2021-10-20
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Hao-Yu Yang , Junjie Bai , Youbing Yin , Qi Song
Abstract: Embodiments of the disclosure provide systems and methods for medical image analysis. A method may include receiving a medical image acquired of a subject by an image acquisition device. The method may also include applying a calcium detection model to detect at least one calcium region relevant in determining a calcium score from the medical image. The method may further include applying a score regression learning model to the at least one calcium region to determine a calcium score for the medical image. The method may additionally include providing the determined calcium score of the medical image for a diagnosis of the subject.
-
Patent Agency Ranking
公开(公告)号：US12119117B2
公开(公告)日：2024-10-15
申请号：US17726307
申请日：2022-04-21
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Bin Kong , Yi Lu , Hao-Yu Yang , Xinyu Guo , Qi Song
CPC classification number: G16H50/30 , G06N3/045 , G06T7/0012 , G06V10/42 , G06V10/44 , G06V10/82 , G16H30/40
Abstract: This disclosure discloses a method and system for predicting disease quantification parameters for an anatomical structure. The method includes extracting a centerline structure based on a medical image. The method further includes predicting the disease quantification parameter for each sampling point on the extracted centerline structure by using a GNN, with each node corresponds to a sampling point on the extracted centerline structure and each edge corresponds to a spatial constraint relationship between the sampling points. For each node, a local feature is extracted based on the image patch for the corresponding sampling point by using a local feature encoder, and a global feature is extracted by using a global feature encoder based on a set of image patches for a set of sampling points, which include the corresponding sampling point and have a spatial constraint relationship defined by the centerline structure. Then, an embed feature is obtained based on both the local feature and the global feature and input into to the node. The method is able to integrate local and global consideration factors of the sampling points into the GNN to improve the prediction accuracy.
-
公开(公告)号：US12094596B2
公开(公告)日：2024-09-17
申请号：US17726039
申请日：2022-04-21
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Xin Wang , Youbing Yin , Bin Kong , Yi Lu , Xinyu Guo , Hao-Yu Yang , Junjie Bai , Qi Song
Abstract: The present disclosure relates to a method and a system for generating anatomical labels of an anatomical structure. The method includes receiving an anatomical structure with an extracted centerline, or a medical image containing the anatomical structure with the extracted centerline; and predicting the anatomical labels of the anatomical structure based on the centerline of the anatomical structure, by utilizing a trained deep learning network. The deep learning network includes a branched network, a Graph Neural Network, a Recurrent Neural Network and a Probability Graph Model, which are connected sequentially in series. The branched network includes at least two branch networks in parallel. The method in the disclosure can automatically generate the anatomical labels of the whole anatomical structure in medical image end to end and provide high prediction accuracy and reliability.
-
公开(公告)号：US12086981B2
公开(公告)日：2024-09-10
申请号：US17557449
申请日：2021-12-21
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Junjie Bai , Hao-Yu Yang , Youbing Yin , Qi Song
CPC classification number: G06T7/0012 , G06T7/11 , G06T2207/20092 , G06T2207/30096 , G06T2207/30101 , G06T2207/30172
Abstract: Embodiments of the disclosure provide systems and methods for analyzing a medical image containing a vessel structure using a sequential model. An exemplary system includes a communication interface configured to receive the medical image and the sequential model. The sequential model includes a vessel extraction sub-model and a lesion analysis sub-model. The vessel extraction sub-model and the lesion analysis sub-model are independently or jointly trained. The exemplary system also includes at least one processor configured to apply the vessel extraction sub-model on the received medical image to extract location information of the vessel structure. The at least one processor also applies the lesion analysis sub-model on the received medical image and the location information extracted by the vessel extraction sub-model to obtain a lesion analysis result of the vessel structure. The at least one processor further outputs the lesion analysis result of the vessel structure.
-
公开(公告)号：US20230099284A1
公开(公告)日：2023-03-30
申请号：US17501041
申请日：2021-10-14
Applicant: Shenzhen Keya Medical Technology Corporation
Inventor： Feng Gao , Hao-Yu Yang , Yue Pan , Youbing Yin , Qi Song
IPC: G16H30/20 , G16H50/30 , G16H50/50 , G16H30/40 , G16H10/60 , G16H50/20 , G06N20/00 , G06T7/00 , A61B5/00 , A61B5/021
Abstract: The disclosure relates to a method, a system, and a computer-readable medium for prognosis management based on medical information of a patient. The method may include receiving the medical information including at least a medical image of the patient reflecting a morphology of an object associated with the patient at a first time, The method may further include predicting a progression condition of the object at a second time based on the medical information of the first time, where the progression condition is indicative of a prognosis risk, and the second time is after the first time. The method may also include generating a prognosis image at the second time reflecting the morphology of the object at the second time based on the medical information of the first time. The method may additionally include providing the progression condition of the object at the second time and the prognosis image at the second time to an information management system for presentation to a user.
-
公开(公告)号：US20220215534A1
公开(公告)日：2022-07-07
申请号：US17557449
申请日：2021-12-21
Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
Inventor： Junjie Bai , Hao-Yu Yang , Youbing Yin , Qi Song
Abstract: Embodiments of the disclosure provide systems and methods for analyzing a medical image containing a vessel structure using a sequential model. An exemplary system includes a communication interface configured to receive the medical image and the sequential model. The sequential model includes a vessel extraction sub-model and a lesion analysis sub-model. The vessel extraction sub-model and the lesion analysis sub-model are independently or jointly trained. The exemplary system also includes at least one processor configured to apply the vessel extraction sub-model on the received medical image to extract location information of the vessel structure. The at least one processor also applies the lesion analysis sub-model on the received medical image and the location information extracted by the vessel extraction sub-model to obtain a lesion analysis result of the vessel structure. The at least one processor further outputs the lesion analysis result of the vessel structure.
-
-
-
-
-
-
-
Search Results
8
records
(took 0.015 seconds)
