Patents by Inventor Bernardo Bizzo

Bernardo Bizzo has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Medical imaging stroke model
    Patent number: 11545266
    Abstract: Systems and techniques for generating and/or employing a medical imaging stroke model are presented. In one example, a system employs a convolutional neural network to generate output data regarding a brain anatomical region based on diffusion-weighted imaging (DWI) data associated with the brain anatomical region and apparent diffusion coefficient (ADC) data associated with the brain anatomical region. The system also detects presence or absence of a medical stroke condition associated with the brain anatomical region based on the output data.
    Type: Grant
    Filed: September 30, 2019
    Date of Patent: January 3, 2023
    Assignees: GE PRECISION HEALTHCARE LLC, PARTNERS HEALTHCARE SYSTEM, INC., THE GENERAL HOSPITAL CORPORATION, THE BRIGHAM AND WOMEN'S HOSPITAL, INC.
    Inventors: John Francis Kalafut, Bernardo Bizzo, Stefano Pedemonte, Christopher Bridge, Neil Tenenholtz, Ramon Gilberto Gonzalez
  • Determining degree of motion using machine learning to improve medical image quality
    Patent number: 11367179
    Abstract: Systems and techniques for determining degree of motion using machine learning to improve medical image quality are presented. In one example, a system generates, based on a convolutional neural network, motion probability data indicative of a probability distribution of a degree of motion for medical imaging data generated by a medical imaging device. The system also determines motion score data for the medical imaging data based on the motion probability data.
    Type: Grant
    Filed: September 30, 2019
    Date of Patent: June 21, 2022
    Assignees: GE Precision Healthcare LLC, Partners Healthcare System, Inc., The General Hospital Corporation, The Bringham and Women's Hospital, Inc.
    Inventors: Jason Polzin, Bernardo Bizzo, Bradley Wright, John Kirsch, Pamela Schaefer
  • Computed tomography medical imaging stroke model
    Patent number: 11331056
    Abstract: Systems and techniques for generating and/or employing a computed tomography (CT) medical imaging stroke model are presented. In one example, a system employs a convolutional neural network to generate learned medical imaging stroke data regarding a brain anatomical region based on CT data associated with the brain anatomical region and diffusion-weighted imaging (DWI) data associated with one or more segmentation masks for the brain anatomical region. The system also detects presence or absence of a medical stroke condition in a CT image based on the learned medical imaging stroke data.
    Type: Grant
    Filed: September 30, 2019
    Date of Patent: May 17, 2022
    Assignees: GE PRECISION HEALTHCARE LLC, PARTNERS HEALTHCARE SYSTEM, INC., THE GENERAL HOSPITAL CORPORATION, THE BRIGHAM AND WOMEN'S HOSPITAL INC.
    Inventors: John Francis Kalafut, Bernardo Bizzo, Romane Gauriau, Michael Lev, Mark Heinz Michalski
  • MEDICAL IMAGING STROKE MODEL
    Publication number: 20210098127
    Abstract: Systems and techniques for generating and/or employing a medical imaging stroke model are presented. In one example, a system employs a convolutional neural network to generate output data regarding a brain anatomical region based on diffusion-weighted imaging (DWI) data associated with the brain anatomical region and apparent diffusion coefficient (ADC) data associated with the brain anatomical region. The system also detects presence or absence of a medical stroke condition associated with the brain anatomical region based on the output data.
    Type: Application
    Filed: September 30, 2019
    Publication date: April 1, 2021
    Inventors: John Francis Kalafut, Bernardo Bizzo, Stefano Pedemonte, Christopher Bridge, Neil Tenenholtz, Ramon Gilberto Gonzalez
  • COMPUTED TOMOGRAPHY MEDICAL IMAGING STROKE MODEL
    Publication number: 20210093258
    Abstract: Systems and techniques for generating and/or employing a computed tomography (CT) medical imaging stroke model are presented. In one example, a system employs a convolutional neural network to generate learned medical imaging stroke data regarding a brain anatomical region based on CT data associated with the brain anatomical region and diffusion-weighted imaging (DWI) data associated with one or more segmentation masks for the brain anatomical region. The system also detects presence or absence of a medical stroke condition in a CT image based on the learned medical imaging stroke data.
    Type: Application
    Filed: September 30, 2019
    Publication date: April 1, 2021
    Inventors: John Francis Kalafut, Bernardo Bizzo, Romane Gauriau, Michael Lev, Mark Heinz Michalski
  • COMPUTED TOMOGRAPHY MEDICAL IMAGING INTRACRANIAL HEMORRHAGE MODEL
    Publication number: 20210093278
    Abstract: Systems and techniques for generating and/or employing a computed tomography (CT) medical imaging intracranial hemorrhage model are presented. In one example, a system employs a convolutional neural network to generate classification output data regarding a brain anatomical region based on computed tomography (CT) data associated with the brain anatomical region. The system also detects presence or absence of a medical intracranial hemorrhage condition in the CT data based on the classification output data. Furthermore, the system determines a subtype of the medical intracranial hemorrhage condition based on the classification output data. The system also generates display data associated with the subtype of the medical intracranial hemorrhage condition in a human-interpretable format.
    Type: Application
    Filed: September 30, 2019
    Publication date: April 1, 2021
    Inventors: John Francis Kalafut, Bernardo Bizzo, Behrooz Hashemian, Christopher Bridge, Neil Tenenholtz, Stuart Robert Pomerantz
  • DETERMINING DEGREE OF MOTION USING MACHINE LEARNING TO IMPROVE MEDICAL IMAGE QUALITY
    Publication number: 20210097679
    Abstract: Systems and techniques for determining degree of motion using machine learning to improve medical image quality are presented. In one example, a system generates, based on a convolutional neural network, motion probability data indicative of a probability distribution of a degree of motion for medical imaging data generated by a medical imaging device. The system also determines motion score data for the medical imaging data based on the motion probability data.
    Type: Application
    Filed: September 30, 2019
    Publication date: April 1, 2021
    Inventors: Jason Polzin, Bernardo Bizzo, Bradley Wright, John Kirsch, Pamela Schaefer