Abhishek D Sharma

20220338816, Oct 27, 2022

Apr 21, 2021

17/236173

- Shanghai, CN
Abhishek Sharma - Cambridge MA, US
Terrence Chen - Cambridge MA, US
Shanhui Sun - Cambridge MA, US

Shanghai United Imaging Intelligence Co., LTD. - Shanghai

A61B 5/00
G06N 3/04
G06N 3/08
G16H 30/40
G16H 50/30

A system and method for cardiac function and myocardial strain analysis include techniques and structure for classifying a set of cardiac images according to their views, detecting a heart range and valid short-axis slices in the set of cardiac images, determining heart segment locations, segmenting heart anatomies for each time frame and each slice, calculating volume related parameters, determining key physiological time points, calculating myocardium transmural thickness and deriving a cardiac function measure from the myocardium transmural thickness at the key physiological time points, estimating a dense motion field from the key physiological time points as applied to the set of cardiac images, calculating myocardial strain along different myocardium directions from the dense motion field, and providing the cardiac function measure and myocardial strain calculation to a user through a user interface.

20220277836, Sep 1, 2022

May 5, 2022

17/737694

- Shanghai, CN
Srikrishna Karanam - Bangalore, IN
Arun Innanje - Lexington MA, US
Shanhui Sun - Lexington MA, US
Abhishek Sharma - Boston MA, US
Yimo Guo - Lexington MA, US
Zhang Chen - Brookline MA, US

SHANGHAI UNITED IMAGING INTELLIGENCE CO., LTD. - Shanghai

G16H 30/40
G06T 7/00
G06T 7/90
G06T 17/00
G06T 7/50
G06T 7/70
G06K 9/62
G06T 17/20
G16H 10/60
G16H 30/20
A61B 5/00
G06V 10/40
G06V 10/42
G06V 20/64
G06V 40/10
G06V 40/20
G06V 20/62

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.

20210272014, Sep 2, 2021

Feb 28, 2020

16/804907

- Shanghai, CN
Ziyan Wu - Cambridge MA, US
Abhishek Sharma - Cambridge MA, US
Arun Innanje - Cambridge MA, US
Terrence Chen - Cambridge MA, US

Shanghai United Imaging Intelligence Co., LTD. - Shanghai

G06N 20/00
H04L 29/06

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.

20210272332, Sep 2, 2021

Feb 28, 2020

16/804985

- Shanghai, CN
Shanhui Sun - Cambridge MA, US
Abhishek Sharma - Cambridge MA, US
Zhang Chen - Cambridge MA, US
Ziyan Wu - Cambridge MA, US

Shanghai United Imaging Intelligence Co., LTD. - Shanghai

G06T 11/00
G06T 1/20
G06T 19/20

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.

20210158937, May 27, 2021

Apr 28, 2020

16/860901

- Shanghai, CN
Srikrishna Karanam - Brighton MA, US
Arun Innanje - Lexington MA, US
Shanhui Sun - Lexington MA, US
Abhishek Sharma - Boston MA, US
Yimo Guo - Lexington MA, US
Zhang Chen - Brookline MA, US

SHANGHAI UNITED IMAGING INTELLIGENCE CO., LTD. - Shanghai

G16H 30/40
G06T 7/00
G06T 7/90
G06T 17/00
G06K 9/46
G06K 9/00

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.

20210130320, May 6, 2021

Jun 11, 2019

17/251771

Abhishek SHARMA - Edison NJ, US
Sarat CHANDARLAPATY - New York NY, US
Lucia WANG - Jersey City NJ, US
Shengjia LIN - Secaucus NJ, US
Weiyi TOY - New York NY, US
John KATZENELLENBOGEN - Urbana IL, US

STEVENS INSTITUTE OF TECHNOLOGY - Hoboken NJ
MEMORIAL SLOAN-KETTERING CANCER CENTER - New York NY
THE BOARD OF TRUSTEES OF THE UNIVERSITY OF ILLINOIS - Urbana IL

C07D 401/04
C07D 209/12
C07C 233/20
C07C 233/21
C07D 333/64
C07D 409/14

A genus of proteolysis-targeting chimeras (PROTACs)-type compounds/antiestrogens has now been found that act as selective estrogen receptor degraders (SERDs) and estrogen receptor antagonists by degrading and antagonizing ERa in breast cancer cells. The compounds are of the following genus: The compounds described herein exhibit anti-proliferative effects, and are potentially useful, alone or in combination with other therapies, for the treatment of breast cancer. In general, these compounds combine a tight binding ERa targeting ligand tethered to a recognition motif or degron. Once bound, the degron recruits destructive cellular components and the targeted receptor (i.e., ERa) is degraded (i.e., destroyed) or antagonized.

20210090736, Mar 25, 2021

Sep 24, 2019

16/580053

- Shanghai, CN
Ziyan Wu - Cambridge MA, US
Abhishek Sharma - Cambridge MA, US
Srikrishna Karanam - Cambridge MA, US

SHANGHAI UNITED IMAGING INTELLIGENCE CO., LTD. - Shanghai

G16H 50/20
G06N 20/00
G16H 30/40

The present disclosure relates to systems and methods for anomaly detection for a medical procedure. The method may include obtaining image data collected by one or more visual sensors via monitoring a medical procedure and a trained machine learning model for anomaly detection. The method may include determining a detection result for the medical procedure based on the image data using the trained machine learning model. The detection result may include whether an anomaly regarding the medical procedure exists. In response to the detection result that the anomaly exists, the method may further include providing feedback relating to the anomaly.

20160055237, Feb 25, 2016

Aug 20, 2014

14/463806

- Cambridge MA, US
Ming-Yu Liu - Revere MA, US
Abhishek Sharma - College Park MD, US

G06F 17/30
G06K 9/62
G06T 7/00
G06K 9/00

A method semantically labels an image acquired of a scene by first obtaining a local semantic feature for each local region in the image. The local semantic features are combined recursively to form intermediate segments until a semantic feature for the entire image is obtained. Then, the semantic feature for the entire image is decombined recursively into intermediate segments until an enhanced semantic feature for each local region is obtained. Then, each local region is labeled according to the enhanced semantic feature.