Shanghai United Imaging Intelligence Co., Ltd.

Abhishek Sharma

Ziyan Wu

Arun Innanje

Meng Zheng

Terrence Chen

Srikrishna Karanam

Shanhui Sun

Zhang Chen

Benjamin Planche

Yimo Guo

Ren Li

Zhongpai Gao

Pierre Sibut-Bourde

A power management apparatus for a workflow to enable low power MR patient positioning on edge devices is disclosed. The power management apparatus changes an operational mode of an edge device from a first power mode to a second power mode after a defined time-interval. The power management apparatus further controls the edge device to capture a first image of a first scene. The power management apparatus further determines a trigger point based on a detection of a plurality of objects in the captured first image. The power management apparatus further changes the operational mode of the edge device from the second power mode to a third power mode to control a consumption of electric power while a set of operations is executed at the edge device. The operational mode of the edge device may be changed at the determined trigger point.

Apparatus and method to enable low power magnetic resonance patient positioning on edge devices

The physical characteristics of one or more anatomical structures of a person may change in accordance with conditions surrounding the determination of such physical characteristics. Machine learning based techniques may be used to determine a template representation of the one or more anatomical structures that may indicate the physical characteristics of the one or more anatomical structures free of the impact imposed by changing conditions. The template representation may then be used to predict the physical characteristics of the one or more anatomical structures under a new set of conditions, without subjecting the person to additional medical scans.

Systems and methods for determining anatomical deformations

The 3D pose of a person may be estimated by triangulating 2D representations of body keypoints (e.g., joint locations) of the person. The triangulation may leverage various metrics such as confidence scores associated with the 2D representations of a keypoint and/or temporal consistency between multiple 3D representations of the keypoint. The 2D representations may be arranged into groups, a candidate 3D representation may be determined for each group, taking into account of the confidence score of each 2D representation in the group, and the candidate 3D representation that has the smallest error may be used to represent the keypoint. Other 3D representation(s) of the keypoint determined from images taken at different times may be used to refine the 3D representation of the keypoint.

Systems and methods for determining 3D human pose based on 2D keypoints

A method and a system for managing healthcare records of a user are provided. The method includes storing an electronic medical record related to the user in form of a non-fungible token (NFT) written to a blockchain, associating a smart contract to the NFT in the blockchain, authorizing a request to access the electronic medical record related to the user based on the defined ownership of the electronic medical record stored in the blockchain, identifying one or more NFTs from the blockchain comprising one or more electronic medical records related to the user based on processing of the identifier information in associated one or more smart contracts therewith, in response to the request, and sending the one or more electronic medical records corresponding to the identified one or more NFTs to a requestor associated with the request.

Patient care record management system

An apparatus for automated collision avoidance includes a sensor configured to detect an object of interest, predicting a representation of the object of interest at a future point in time, calculating an indication of a possibility of a collision with the object of interest based on the representation of the object of interest at the future point in time, and executing a collision avoidance action based on the indication.

Automated collision avoidance in medical environments

A medical system may utilize a modular and extensible sensing device to derive a two-dimensional (2D) or three-dimensional (3D) human model for a patient in real-time based on images of the patient captured by a sensor such as a digital camera. The 2D or 3D human model may be visually presented on one or more devices of the medical system and used to facilitate a healthcare service provided to the patient. In examples, the 2D or 3D human model may be used to improve the speed, accuracy and consistency of patient positioning for a medical procedure. In examples, the 2D or 3D human model may be used to enable unified analysis of the patient's medical conditions by linking different scan images of the patient through the 2D or 3D human model. In examples, the 2D or 3D human model may be used to facilitate surgical navigation, patient monitoring, process automation, and/or the like.

Sensing device for medical facilities

The shape and/or location of an organ may change in accordance with changes in the body shape and/or pose of a patient. Described herein are systems, methods, and instrumentalities for automatically determining, using an artificial neural network (ANN), the shape and/or location of the organ based on human models that reflect the body shape and/or pose the patient. The ANN may be trained to learn the spatial relationship between the organ and the body shape or pose of the patient. Then, at an inference time, the ANN may be used to determine the relationship based on a first patient model and a first representation (e.g., a point cloud) of the organ so that given a second patient model thereafter, the ANN may automatically determine the shape and/or location of the organ corresponding to the body shape or pose of the patient indicated by the second patient model.

Automatic organ geometry determination

Data samples are transmitted from a central server to at least one local server apparatus. The central server receives a set of predictions from the at least one local server apparatus that are based on the transmitted set of data samples. The central server trains a central model based on the received set of predictions. The central model, or a portion of the central model corresponding to a task of interest, can then be sent to the at least one local server apparatus. Neither local data from local sites nor trained models from the local sites are transmitted to the central server. This ensures protection and security of data at the local sites.

System and methods for privacy preserving cross-site federated learning

Automatically validating the calibration of an visual sensor network includes acquiring image data from visual sensors that have partially overlapping fields of view, extracting a representation of an environment in which the visual sensors are disposed, calculating one or more geometric relationships between the visual sensors, comparing the calculated one or more geometric relationships with previously obtained calibration information of the visual sensors, and verifying a current calibration of the visual sensors based on the comparison.

Automated calibration and validation of sensor network

Image-based key points detection using a convolutional neural network (CNN) may be impacted if the key points are occluded in the image. Images obtained from additional imaging modalities such as depth and/or thermal images may be used in conjunction with RGB images to reduce or minimize the impact of the occlusion. The additional images may be used to determine adjustment values that are then applied to the weights of the CNN so that the convolution operations may be performed in a modality aware manner to increase the robustness, accuracy, and efficiency of key point detection.

Key points detection using multiple image modalities

Systems, methods and instrumentalities are described herein for automating a medical environment. The automation may be realized using one or more sensing devices and at least one processing device. The sensing devices may be configured to capture images of the medical environment and provide the images to the processing device. The processing device may determine characteristics of the medical environment based on the images and automate one or more aspects of the operations in the medical environment. These characteristics may include, e.g., people and/or objects present in the images and respective locations of the people and/or objects in the medical environment. The operations that may be automated may include, e.g., maneuvering and/or positioning a medical device based on the location of a patient, determining and/or adjusting the parameters of a medical device, managing a workflow, providing instructions and/or alerts to a patient or a physician, etc.

Automating a medical environment

Automated patient positioning and modelling includes a hardware processor to obtain image data from an imaging sensor, classify the image data, using a first machine learning model, as a patient pose based on one or more pre-defined protocols for patient positioning, provide a confidence score based on the classification of the image data and if the confidence score is less than a pre-determined value, re-classify the image data using a second machine learning model; or if the confidence score is greater than a pre-determined value, identify the image data as corresponding to a patient pose based on one or more pre-defined protocols for patient positioning during a scan procedure.

Automated patient modeling and positioning

Disclosed is a system and a method for configuring an IP device to be discoverable to a client device over a local network having a DHCP server for assigning dynamic IP addresses. The method includes obtaining a dynamic IP address assigned to the IP device upon completion of boot process for the IP device; checking if a static IP address has been set for the IP device; determining if the dynamic IP address and the static IP address are in a same subnet of the local network; implementing the static IP address set for the IP device, if the dynamic IP address and the static IP address are in the same subnet of the local network; and implementing the dynamic IP address assigned to the IP device, if the dynamic IP address and the static IP address are not in the same subnet of the local network.

System and method for configuring internet protocol device

A standalone image reconstruction device is configured to reconstruct the raw signals received from a radiology scanner device into a reconstructed output signal. The image reconstruction device is a vendor neutral interface between the radiology scanner device and the post processing imaging device. The reconstructed output signal is a user readable domain that can be used to generate a medical image or a three-dimensional (3D) volume. The apparatus is configured to reconstruct signals from different types of radiology scanner devices using any suitable image reconstruction protocol.

Plugin and dynamic image modality reconstruction interface device

Methods and systems for organizing medical data. For example, a computer-implemented method includes receiving first data of a first data category, the first data having a first data format; extracting a first plurality of attributes from the first data using a first extractor; mapping the first plurality of attributes to an unified data format using a first mapper; receiving second data of a second data category, the second data having a second data format; extracting a second plurality of attributes from the second data using a second extractor; mapping the second plurality of attributes to the unified data format using a second mapper; and building an ontology for a use case by at least linking the first plurality of attributes and the second plurality of attributes.

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Boston, MA, United States of America

Abhishek Sharma