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公开(公告)号：US11751832B2
公开(公告)日：2023-09-12
申请号：US17083761
申请日：2020-10-29
Applicant: GE Precision Healthcare LLC , Partners Healthcare System, Inc. , The General Hospital Corporation , The Brigham and Women's Hospital, Inc.
Inventor： Markus Daniel Herrmann , John Francis Kalafut , Bernardo Canedo Bizzo , Christopher P. Bridge , Michael Lev , Charles J. Lu , James Hillis
CPC classification number: A61B6/507 , A61B6/032 , A61B6/501 , A61B6/504 , A61B6/5217 , G06N20/20 , G06T7/70 , G16H30/20 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2207/20132 , G06T2207/30101
Abstract: Systems and techniques that facilitate automated localization of large vessel occlusions are provided. In various embodiments, an input component can receive computed tomography angiogram (CTA) images of a patient's brain. In various embodiments, a localization component can determine, via a machine learning algorithm, a location of a large vessel occlusion (LVO) in the patient's brain based on the CTA images. In various instances, the location of the LVO can comprise a laterality and an occlusion site. In various aspects, the laterality can indicate a right side or a left side of the patient's brain, and the occlusion site can indicate an internal carotid artery (ICA), an M1 segment of a middle cerebral artery (MCA) or an M2 segment of an MCA. In various cases, a visualization component can generate and display to a user a three-dimensional maximum intensity projection (MIP) reconstruction of the patient's brain based on the CTA images to facilitate visual verification of the LVO by the user.
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2.发明公开公开(公告)号：US20230237369A1
公开(公告)日：2023-07-27
申请号：US17585903
申请日：2022-01-27
Applicant: GE Precision Healthcare LLC , Partners Healthcare System, Inc. , The General Hospital Corporation , The Brigham and Women's Hospital, Inc.
Inventor： Ezra Nathaniel Ojeda Rodriguez , Edward H Lail , Steven Guitron , Oleg Pianykh , Vamsee Rangavajhala , Murat Akturk
CPC classification number: G06N20/00 , G06Q10/1095
Abstract: Systems/techniques that facilitate automated training of machine learning classification for patient missed care opportunities or late arrivals are provided. In various embodiments, a system can access a set of annotated data candidates defined by two or more feature categories. In various aspects, the system can train a machine learning classifier on the set of annotated data candidates, thereby causing internal parameters of the machine learning classifier to become iteratively updated. In various instances, the system rank the two or more feature categories in order of classification importance, based on the iteratively updated internal parameters of the machine learning classifier. In various cases, the system can perform one or more electronic actions based on the two or more feature categories being ranked in order of classification importance.
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公开(公告)号：US11367179B2
公开(公告)日：2022-06-21
申请号：US16588129
申请日：2019-09-30
Applicant: GE Precision Healthcare LLC , Partners Healthcare System, Inc. , The General Hospital Corporation , The Brigham and Women's Hospital, Inc.
Inventor： Jason Polzin , Bernardo Bizzo , Bradley Wright , John Kirsch , Pamela Schaefer
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.
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公开(公告)号：US20210236080A1
公开(公告)日：2021-08-05
申请号：US17083761
申请日：2020-10-29
Applicant: GE Precision Healthcare LLC , Partners HealthCare System, Inc. , The General Hospital Corporation , The Brigham and Women's Hospital, Inc.
Inventor： Markus Daniel Herrmann , John Francis Kalafut , Bernardo Canedo Bizzo , Christopher P. Bridge , Michael Lev , Charles J. Lu , James Hillis
Abstract: Systems and techniques that facilitate automated localization of large vessel occlusions are provided. In various embodiments, an input component can receive computed tomography angiogram (CTA) images of a patient's brain. In various embodiments, a localization component can determine, via a machine learning algorithm, a location of a large vessel occlusion (LVO) in the patient's brain based on the CTA images. In various instances, the location of the LVO can comprise a laterality and an occlusion site. In various aspects, the laterality can indicate a right side or a left side of the patient's brain, and the occlusion site can indicate an internal carotid artery (ICA), an M1 segment of a middle cerebral artery (MCA) or an M2 segment of an MCA. In various cases, a visualization component can generate and display to a user a three-dimensional maximum intensity projection (MIP) reconstruction of the patient's brain based on the CTA images to facilitate visual verification of the LVO by the user.
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公开(公告)号：US20210097679A1
公开(公告)日：2021-04-01
申请号：US16588129
申请日：2019-09-30
Applicant: GE Precision Healthcare LLC , Partners HealthCare System, Inc. , The General Hospital Corporation , The Brigham and Women's Hospital, Inc.
Inventor： Jason Polzin , Bernardo Bizzo , Bradley Wright , John Kirsch , Pamela Schaefer
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.
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Patent Agency Ranking
公开(公告)号：US20210093278A1
公开(公告)日：2021-04-01
申请号：US16587828
申请日：2019-09-30
Applicant: GE Precision Healthcare LLC , Partners HealthCare System, Inc. , The General Hospital Corporation , The Brigham and Women's Hospital, Inc.
Inventor： John Francis Kalafut , Bernardo Bizzo , Behrooz Hashemian , Christopher Bridge , Neil Tenenholtz , Stuart Robert Pomerantz
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.
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公开(公告)号：US20210093258A1
公开(公告)日：2021-04-01
申请号：US16588013
申请日：2019-09-30
Applicant: GE Precision Healthcare LLC , Partners HealthCare System, Inc. , The General Hospital Corporation , The Brigham and Women's Hospital, Inc.
Inventor： John Francis Kalafut , Bernardo Bizzo , Romane Gauriau , Michael Lev , Mark Heinz Michalski
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.
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公开(公告)号：US20130236533A1
公开(公告)日：2013-09-12
申请号：US13844258
申请日：2013-03-15
Applicant: MASSACHUSETTS INSTITUTE OF TECHNOLOGY , PRESIDENT AND FELLOWS OF HARVARD COLLEGE , PARTNERS HEALTHCARE SYSTEM, INC. , IMMUNE DISEASE INSTITUTE
Inventor： Ulrich H. von Andrian , Omid C. Farokhzad , Robert S. Langer , Tobias Junt , Elliott Ashley Moseman , Liangfang Zhang , Pamela Basto , Matteo Iannacone , Frank Alexis
IPC: A61K39/39
CPC classification number: A61K9/14 , A61K39/00 , A61K39/0011 , A61K39/12 , A61K39/39 , A61K47/6911 , A61K47/6937 , A61K2039/555 , A61K2039/60 , Y02A50/464 , Y02A50/472
Abstract: The present invention provides compositions and systems for delivery of nanocarriers to cells of the immune system. The invention provides vaccine nanocarriers capable of stimulating an immune response in T cells and/or B cells, in some embodiments, comprising at least one immunomodulatory agent, and optionally comprising at last one targeting moiety and optionally at least one immunostimulatory agent. The invention provides pharmaceutical compositions comprising inventive vaccine nanocarriers. The present invention provides methods of designing, manufacturing, and using inventive vaccine nanocarriers and pharmaceutical compositions thereof. The invention provides methods of prophylaxis and/or treatment of diseases, disorders, and conditions comprising administering at least one inventive vaccine nanocarrier to a subject in need thereof.
Abstract translation: 本发明提供用于将纳米载体递送至免疫系统细胞的组合物和系统。 本发明提供了在一些实施方案中能够刺激T细胞和/或B细胞中的免疫应答的疫苗纳米载体，其包含至少一种免疫调节剂，并且任选地包含最后一个靶向部分和任选的至少一种免疫刺激剂。 本发明提供包含本发明疫苗纳米载体的药物组合物。 本发明提供了设计，制造和使用本发明疫苗纳米载体及其药物组合物的方法。 本发明提供了预防和/或治疗疾病，病症和病症的方法，包括向有需要的受试者施用至少一种本发明的疫苗纳米载体。
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公开(公告)号：US20240379239A1
公开(公告)日：2024-11-14
申请号：US18313931
申请日：2023-05-08
Applicant: GE Precision Healthcare LLC , Partners HealthCare System, Inc. , The General Hospital Corporation , The Brigham and Women’s Hospital, Inc.
Inventor： Eigil Samset , Xiang Li , Quanzheng Li , Michael H. Picard , Hui Ren , Carola Alejandra Maraboto Gonzalez , Jerome Charton , Abhijit Patil , Mark James Perkins
Abstract: Techniques are described for computer-implemented techniques for managing various aspects of the cardiac care pathway using machine learning. According to an embodiment, a method can include training an outcomes forecasting model to predict patient outcomes resulting from undergoing a cardiac valve procedure using multi-modal training data for a plurality of different patients, wherein the training comprising separately training different machine learning sub-models of the forecasting model to predict preliminary patient outcome data and mapping the preliminary patient outcome data to the patient outcomes, resulting in a trained version of the outcome forecasting model. The method further includes applying the trained version of the outcomes forecasting model to new multi-modal data for a new patient to predict the patient outcomes for the new patient resulting from undergoing the cardiac value procedure.
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公开(公告)号：US11545266B2
公开(公告)日：2023-01-03
申请号：US16588080
申请日：2019-09-30
Applicant: GE Precision Healthcare LLC , Partners HealthCare System, Inc. , The General Hospital Corporation , The Brigham and Women's Hospital, Inc.
Inventor： John Francis Kalafut , Bernardo Bizzo , Stefano Pedemonte , Christopher Bridge , Neil Tenenholtz , Ramon Gilberto Gonzalez
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.
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