公开(公告)号：US20230031328A1

公开(公告)日：2023-02-02

申请号：US17559282

申请日：2021-12-22

Applicant: GE Precision Healthcare LLC ,  Partners Healthcare System, Inc. ,  The General Hospital Corporation ,  The Brigham and Women's Hospital, Inc.

Inventor： Hariharan Ravishankar ,  Abhijit Patil ,  Rohit Pardasani ,  Dirk Johannes Varelmann ,  Pankaj Sarin ,  Marcio Aloisio Bezerra Cavalcanti Rockenbach ,  Quanzheng Li

IPC: G16H50/20 ,  G06N20/00 ,  A61M16/00 ,  A61M16/10

Abstract: Systems and techniques for monitoring, predicting and/or alerting for short-term oxygen support needs of patients are presented. A system can include a data collection component that receives multimodal patient data for a patient having a respiratory condition in association with monitoring and treating the respiratory condition in real-time, the multimodal patient data comprising at least physiological data regarding physiological parameters tracked for the patient over a period of time, and current oxygen support data regarding a current oxygen support mechanism of the patient. The system can further include an oxygen support forecasting component that processes the multimodal patient data using an oxygen support forecasting model to generate an output forecast that indicates whether a change to the current oxygen support mechanism is recommended for the patient within a defined upcoming timeframe

公开(公告)号：US20210327566A1

公开(公告)日：2021-10-21

申请号：US17364544

申请日：2021-06-30

Applicant: GE Precision Healthcare LLC

Inventor： Hariharan Ravishankar ,  Dattesh Dayanand Shanbhag

IPC: G16H30/40 ,  G16H30/20 ,  A61B5/055 ,  A61B6/03 ,  G06N3/04 ,  G06T7/00 ,  G06T11/00

Abstract: Methods and systems are provided for reconstructing images from measurement data using one or more deep neural networks according to a decimation strategy. In one embodiment, a method for reconstructing an image using measurement data comprises, receiving measurement data acquired by an imaging device, selecting a decimation strategy, producing a reconstructed image from the measurement data using the decimation strategy and one or more deep neural networks, and displaying the reconstructed image via a display device. By decimating measurement data to form one or more decimated measurement data arrays, a computational complexity of mapping the measurement data to image data may be reduced from O(N4), where N is the size of the measurement data, to O(M4), where M is the size of an individual decimated measurement data array, wherein M

公开(公告)号：US20210174496A1

公开(公告)日：2021-06-10

申请号：US16703547

申请日：2019-12-04

Applicant: GE Precision Healthcare LLC

Inventor： Pavan Annangi ,  Hariharan Ravishankar ,  Tore Bjaastad ,  Erik Normann Steen ,  Svein Arne Aase ,  Rohan Patil

IPC: G06T7/00 ,  A61B8/08

Abstract: Methods and systems are provided for sequentially selecting scan parameter values for ultrasound imaging. In one example, a method includes selecting a first parameter value for the a first scan parameter based on an image quality of each ultrasound image of a first plurality of ultrasound images of an anatomical region, each ultrasound image of the first plurality of ultrasound images having a different parameter value for the first scan parameter, selecting a second parameter value for a second scan parameter based on an image quality of each ultrasound image of a second plurality of ultrasound images of the anatomical region, each ultrasound image of the second plurality of ultrasound images having a different parameter value for the second scan parameter, and applying the first parameter value for the first scan parameter and the second parameter value for the second scan parameter to one or more additional ultrasound images.

公开(公告)号：US20250104451A1

公开(公告)日：2025-03-27

申请号：US18471836

申请日：2023-09-21

Applicant: GE Precision Healthcare LLC

Inventor： Hariharan Ravishankar ,  Vikram Reddy Melapudi ,  Pavan Annangi ,  Abhijit Patil

IPC: G06V20/70 ,  G06N5/04 ,  G06V10/774 ,  G06V10/776 ,  G16H10/60 ,  G16H30/40

Abstract: An iterative framework for learning multimodal mappings tailored to medical image inferencing tasks is provided. In an example, a computer-implemented method can comprise receiving multimodal annotation data for medical images, the multimodal annotation data comprising non-image annotation data and image annotation data, and employing one or more machine learning (ML) processes to learn bi-directional mappings between non-image features included in the non-image annotation data and image features associated with the medical images and the image annotation data. The method further comprises generating, as a result of the one or more ML processes, a model configured to: infer one or more of the non-image features associated with new medical images given the new medical images, and/or infer one or more of the image features associated with the new medical images given the new medical images and non-image input corresponding to at least some of the non-image annotation data.

公开(公告)号：US20240428567A1

公开(公告)日：2024-12-26

申请号：US18340246

申请日：2023-06-23

Applicant: GE Precision Healthcare LLC

Inventor： Pavan Annangi ,  Vikram Reddy Melapudi ,  Hariharan Ravishankar ,  Deepa Anand

IPC: G06V10/774 ,  G06T7/00 ,  G06V10/776 ,  G06V10/82

Abstract: Techniques are described for refining or updating medical image inferencing models post deployment using synthetic images generated from non-image data feedback. In an example, a system can comprise a memory that stores computer-executable components and a processor that executes the computer-executable components stored in the memory. The computer-executable components can comprise an image generation component that generates synthetic medical images based on feedback information associated with performance of a medical image inferencing model received in association with application of the medical image inferencing model to medical images in a deployment environment, wherein the feedback information excludes image data. The computer-executable components can further comprise a refinement component that updates the medical image inferencing model using the synthetic images and a model updating process.

公开(公告)号：US20240428421A1

公开(公告)日：2024-12-26

申请号：US18337968

申请日：2023-06-20

Applicant: GE Precision Healthcare LLC

Inventor： Krishna Seetharam Shriram ,  Vikram Reddy Melapudi ,  Hariharan Ravishankar ,  Pavan Annangi ,  Chandan Kumar Mallappa Aladahalli

IPC: G06T7/12 ,  G06T7/00 ,  G06T7/13 ,  G06V10/25

Abstract: Systems/techniques that facilitate improved uncertainty estimation via object-specific and object-agnostic segmentation disagreement are provided. In various embodiments, a system can access an image depicting an object. In various aspects, the system can localize, via execution of an object-specific segmentation model on the image, a first inferred boundary of the object. In various instances, the system can generate an uncertainty score for the first inferred boundary, based on a second inferred boundary of the object generated via execution of an object-agnostic segmentation model on the image.

公开(公告)号：US20230386676A1

公开(公告)日：2023-11-30

申请号：US18312919

申请日：2023-05-05

Applicant: GE Precision Healthcare LLC

Inventor： Hariharan Ravishankar ,  Rohan Patil ,  Abhijit Patil

IPC: G16H50/30 ,  G16H40/60 ,  G06N3/04 ,  G06N3/08

CPC classification number: G16H50/30 ,  G16H40/60 ,  G06N3/04 ,  G06N3/08

Abstract: The disclosure relates generally to a patient monitoring device and, more particularly, to improved system and method to detect a false alarm in a patient monitoring device. The disclosure specifically relates to a system and a method to detect a false alarm in a patient monitoring device. The system may include a patient monitoring device configured to receive a patient monitoring data from a patient. The system may enable the processing of the patient monitoring data by a processing device to determine a false alarm generated by the patient monitoring device. The system may further provide a user-interface, which may be configured to filter a true alarm from a false alarm generated by the patient monitoring device.

公开(公告)号：US20230238134A1

公开(公告)日：2023-07-27

申请号：US17648920

申请日：2022-01-25

Applicant: GE Precision Healthcare LLC

Inventor： Hariharan Ravishankar ,  Rohan Keshav Patil ,  Abhijit Patil ,  Heikki Paavo Aukusti Vaananen

IPC: G16H50/20 ,  G06N3/04 ,  G16H15/00 ,  G16H40/63 ,  G16H50/30 ,  A61B5/361 ,  A61B5/363

CPC classification number: G16H50/20 ,  G06N3/0454 ,  G16H15/00 ,  G16H40/63 ,  G16H50/30 ,  A61B5/361 ,  A61B5/363

Abstract: Methods and systems are provided for predicting cardiac arrhythmias based on multi-modal patient monitoring data via deep learning. In an example, a method may include predicting an imminent onset of a cardiac arrhythmia in a patient, before the cardiac arrhythmia occurs, by analyzing patient monitoring data via a multi-arm deep learning model, outputting an arrhythmia event in response to the prediction, and outputting a report indicating features of the patient monitoring data contributing to the prediction. In this way, the multi-arm deep learning model may predict cardiac arrhythmias before their onset.

公开(公告)号：US11699515B2

公开(公告)日：2023-07-11

申请号：US17364544

申请日：2021-06-30

Applicant: GE Precision Healthcare LLC

Inventor： Hariharan Ravishankar ,  Dattesh Dayanand Shanbhag

IPC: G16H30/40 ,  G16H30/20 ,  A61B5/055 ,  A61B6/03 ,  G06T7/00 ,  G06T11/00 ,  G06N3/045

CPC classification number: G16H30/40 ,  A61B5/055 ,  A61B6/032 ,  G06N3/045 ,  G06T7/0014 ,  G06T11/008 ,  G16H30/20 ,  G06T2207/10081 ,  G06T2207/10084 ,  G06T2207/10088

Abstract: Methods and systems are provided for reconstructing images from measurement data using one or more deep neural networks according to a decimation strategy. In one embodiment, a method for reconstructing an image using measurement data comprises, receiving measurement data acquired by an imaging device, selecting a decimation strategy, producing a reconstructed image from the measurement data using the decimation strategy and one or more deep neural networks, and displaying the reconstructed image via a display device. By decimating measurement data to form one or more decimated measurement data arrays, a computational complexity of mapping the measurement data to image data may be reduced from O(N4), where N is the size of the measurement data, to O(M4), where M is the size of an individual decimated measurement data array, wherein M

公开(公告)号：US11308609B2

公开(公告)日：2022-04-19

申请号：US16703547

申请日：2019-12-04

Applicant: GE Precision Healthcare LLC

Inventor： Pavan Annangi ,  Hariharan Ravishankar ,  Tore Bjaastad ,  Erik Normann Steen ,  Svein Arne Aase ,  Rohan Patil

IPC: G06T7/00 ,  A61B8/08

Abstract: Methods and systems are provided for sequentially selecting scan parameter values for ultrasound imaging. In one example, a method includes selecting a first parameter value for the a first scan parameter based on an image quality of each ultrasound image of a first plurality of ultrasound images of an anatomical region, each ultrasound image of the first plurality of ultrasound images having a different parameter value for the first scan parameter, selecting a second parameter value for a second scan parameter based on an image quality of each ultrasound image of a second plurality of ultrasound images of the anatomical region, each ultrasound image of the second plurality of ultrasound images having a different parameter value for the second scan parameter, and applying the first parameter value for the first scan parameter and the second parameter value for the second scan parameter to one or more additional ultrasound images.