公开(公告)号：US12249023B2

公开(公告)日：2025-03-11

申请号：US18065964

申请日：2022-12-14

Applicant: GE Precision Healthcare LLC

Inventor： Deepa Anand ,  Bipul Das ,  Vanika Singhal ,  Rakesh Mullick ,  Sanjay Kumar NT

IPC: G06T15/20 ,  G06T15/08 ,  G06V10/82

Abstract: Systems/techniques that facilitate interpretable task-specific dimensionality-reduction are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can generate, via execution of a first deep learning neural network, a voxel-wise weight map corresponding to the three-dimensional medical image and a set of projection vectors corresponding to the three-dimensional medical image. In various instances, the system can generate a set of two-dimensional projection images of the three-dimensional medical image, based on the voxel-wise weight map and the set of projection vectors. In various cases, the first deep learning neural network can be trained in a serial pipeline with a second deep learning neural network that is configured to perform an inferencing task on two-dimensional inputs. This can cause the set of two-dimensional projection images to be tailored to the inferencing task.

公开(公告)号：US20250072826A1

公开(公告)日：2025-03-06

申请号：US18458825

申请日：2023-08-30

Applicant: GE Precision Healthcare LLC

Inventor： Aanchal Mongia ,  Sanjay Kumar NT ,  Abhijit Patil ,  John D. Page ,  Sai Sudha ,  Sandeep S. Kurup ,  Badari Prasad KG ,  Andrea Vitagliano ,  Tom Häggblom

IPC: A61B5/00 ,  A61B5/08 ,  G16H40/20

Abstract: Systems are provided for perioperative care. In an example, a system includes one or more processors and memory storing instructions executable by the one or more processors to output a graphical user interface (GUI) including a first visual representation of a default or previously-determined phase of anesthesia delivery for a patient; receive a plurality of monitoring parameters of the patient over an observation window, at least a portion of the plurality of monitoring parameters obtained from an anesthesia delivery machine; identify, by applying a selected set of rules to the plurality of monitoring parameters, whether an event signaling a change to a new phase of anesthesia delivery for the patient is detected; based on the event being detected, update the GUI to display a second visual representation of the new phase; and based on the event not being detected, maintain the first visual representation on the GUI.

公开(公告)号：US20240203039A1

公开(公告)日：2024-06-20

申请号：US18065964

申请日：2022-12-14

Applicant: GE Precision Healthcare LLC

Inventor： Deepa Anand ,  Bipul Das ,  Vanika Singhal ,  Rakesh Mullick ,  Sanjay Kumar NT

IPC: G06T15/20 ,  G06T15/08 ,  G06V10/82

CPC classification number: G06T15/20 ,  G06T15/08 ,  G06V10/82

Abstract: Systems/techniques that facilitate interpretable task-specific dimensionality-reduction are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can generate, via execution of a first deep learning neural network, a voxel-wise weight map corresponding to the three-dimensional medical image and a set of projection vectors corresponding to the three-dimensional medical image. In various instances, the system can generate a set of two-dimensional projection images of the three-dimensional medical image, based on the voxel-wise weight map and the set of projection vectors. In various cases, the first deep learning neural network can be trained in a serial pipeline with a second deep learning neural network that is configured to perform an inferencing task on two-dimensional inputs. This can cause the set of two-dimensional projection images to be tailored to the inferencing task.