公开(公告)号：US11727086B2

公开(公告)日：2023-08-15

申请号：US17093960

申请日：2020-11-10

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Gopal B. Avinash ,  Máté Fejes ,  Ravi Soni ,  Dániel Attila Szabó ,  Rakesh Mullick ,  Vikram Melapudi ,  Krishna Seetharam Shriram ,  Sohan Rashmi Ranjan ,  Bipul Das ,  Utkarsh Agrawal ,  László Ruskó ,  Zita Herczeg ,  Barbara Darázs

IPC: G06F18/214 ,  G06T7/30 ,  G06N5/04 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  A61B6/03 ,  A61B6/00 ,  A61B5/055 ,  A61B5/00 ,  G06T5/50 ,  G06F18/22 ,  G06F18/28 ,  G06F18/21

CPC classification number: G06F18/214 ,  A61B5/055 ,  A61B5/7267 ,  A61B6/032 ,  A61B6/5223 ,  G06F18/2178 ,  G06F18/22 ,  G06F18/28 ,  G06N5/04 ,  G06T5/50 ,  G06T7/30 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20212 ,  G06T2207/30004 ,  G06V2201/03

Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

公开(公告)号：US20220092768A1

公开(公告)日：2022-03-24

申请号：US17122709

申请日：2020-12-15

Applicant: GE Precision Healthcare LLC

Inventor： Vikram Melapudi ,  Bipul Das ,  Krishna Seetharam Shriram ,  Prasad Sudhakar ,  Rakesh Mullick ,  Sohan Rashmi Ranjan ,  Utkarsh Agarwal

IPC: G06T7/00 ,  G06T5/00 ,  G06T7/10 ,  G06T11/00 ,  A61B6/00

Abstract: Techniques are provided for generating enhanced image representations from original X-ray images using deep learning techniques. In one embodiment, a system is provided that includes a memory storing computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a reception component, an analysis component, and an artificial intelligence component. The analysis component analyzes the original X-ray image using an AI-based model with respect to a set of features of interest. The AI component generates a plurality of enhanced image representations. Each enhanced image representation highlights a subset of the features of interest and suppresses remaining features of interest in the set that are external to the subset.

公开(公告)号：US11657501B2

公开(公告)日：2023-05-23

申请号：US17122709

申请日：2020-12-15

Applicant: GE Precision Healthcare LLC

Inventor： Vikram Melapudi ,  Bipul Das ,  Krishna Seetharam Shriram ,  Prasad Sudhakar ,  Rakesh Mullick ,  Sohan Rashmi Ranjan ,  Utkarsh Agarwal

IPC: G06T7/00 ,  G06T5/00 ,  G06T7/10 ,  G06T11/00 ,  A61B6/00

CPC classification number: G06T7/0012 ,  A61B6/482 ,  G06T5/00 ,  G06T7/10 ,  G06T11/003 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/10116 ,  G06T2207/20081

Abstract: Techniques are provided for generating enhanced image representations from original X-ray images using deep learning techniques. In one embodiment, a system is provided that includes a memory storing computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a reception component, an analysis component, and an artificial intelligence component. The analysis component analyzes the original X-ray image using an AI-based model with respect to a set of features of interest. The AI component generates a plurality of enhanced image representations. Each enhanced image representation highlights a subset of the features of interest and suppresses remaining features of interest in the set that are external to the subset.

公开(公告)号：US20230342427A1

公开(公告)日：2023-10-26

申请号：US18343266

申请日：2023-06-28

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Gopal B. Avinash ,  Máté Fejes ,  Ravi Soni ,  Dániel Attila Szabó ,  Rakesh Mullick ,  Vikram Melapudi ,  Krishna Seetharam Shriram ,  Sohan Rashmi Ranjan ,  Bipul Das ,  Utkarsh Agrawal ,  László Ruskó ,  Zita Herczeg ,  Barbara Darázs

IPC: G06F18/214 ,  G06N5/04 ,  G16H30/40 ,  A61B5/055 ,  G06T5/50 ,  G06F18/21 ,  G06T7/30 ,  A61B5/00 ,  G16H30/20 ,  G16H50/20 ,  G16H50/50 ,  A61B6/03 ,  G06F18/22 ,  G06F18/28 ,  A61B6/00

CPC classification number: G06F18/214 ,  A61B5/055 ,  A61B5/7267 ,  A61B6/032 ,  A61B6/5223 ,  G06F18/2178 ,  G06F18/22 ,  G06F18/28 ,  G06N5/04 ,  G06T5/50 ,  G06T7/30 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20212 ,  G06T2207/30004 ,  G06V2201/03

Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

公开(公告)号：US11666288B2

公开(公告)日：2023-06-06

申请号：US16802366

申请日：2020-02-26

Applicant: GE Precision Healthcare LLC

Inventor： Sidharth Abrol ,  John Page ,  Sohan Rashmi Ranjan ,  Abhijit Patil

IPC: G06F3/00 ,  A61B5/00 ,  G16H40/67 ,  G16H50/30 ,  G06F3/0482

CPC classification number: A61B5/7435 ,  A61B5/7275 ,  G16H40/67 ,  G16H50/30 ,  G06F3/0482

Abstract: Systems and methods are provided for perioperative care in a medical facility. In an example, a system includes a display and a computing device operably coupled to the display and storing instructions executable to output, to the display, a graphical user interface (GUI) that includes real-time medical device data of a patient, at least some of the real-time medical device data displayed via the GUI as a plurality of patient monitoring parameter tiles, the GUI including a risk score indicative of a relative likelihood that the patient will exhibit a condition within a period of time, and responsive to a user input, display, on the GUI, a set of trend lines each showing values for a respective patient monitoring parameter over a time range, each trend line of the set of trend lines selected based on a contribution of each respective patient monitoring parameter to the risk score.

公开(公告)号：US20220101048A1

公开(公告)日：2022-03-31

申请号：US17093960

申请日：2020-11-10

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Gopal B. Avinash ,  Máté Fejes ,  Ravi Soni ,  Dániel Attila Szabó ,  Rakesh Mullick ,  Vikram Melapudi ,  Krishna Seetharam Shriram ,  Sohan Rashmi Ranjan ,  Bipul Das ,  Utkarsh Agrawal ,  László Ruskó ,  Zita Herczeg ,  Barbara Darázs

IPC: G06K9/62 ,  G06T5/50 ,  G06T7/30 ,  G06N5/04 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  A61B6/03 ,  A61B6/00 ,  A61B5/055 ,  A61B5/00

Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

公开(公告)号：US20210059616A1

公开(公告)日：2021-03-04

申请号：US16802366

申请日：2020-02-26

Applicant: GE Precision Healthcare LLC

Inventor： Sidharth Abrol ,  John Page ,  Sohan Rashmi Ranjan ,  Abhijit Patil

IPC: A61B5/00 ,  G16H40/67 ,  G16H50/30

Abstract: Systems and methods are provided for perioperative care in a medical facility. In an example, a system includes a display and a computing device operably coupled to the display and storing instructions executable to output, to the display, a graphical user interface (GUI) that includes real-time medical device data of a patient, at least some of the real-time medical device data displayed via the GUI as a plurality of patient monitoring parameter tiles, the GUI including a risk score indicative of a relative likelihood that the patient will exhibit a condition within a period of time, and responsive to a user input, display, on the GUI, a set of trend lines each showing values for a respective patient monitoring parameter over a time range, each trend line of the set of trend lines selected based on a contribution of each respective patient monitoring parameter to the risk score.