-
公开(公告)号：US10786208B2
公开(公告)日：2020-09-29
申请号：US16577380
申请日：2019-09-20
申请人： TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC. , THE REGENTS OF THE UNIVERSITY OF MICHIGAN
IPC分类号： G08B23/00 , A61B5/00 , A61B5/0456 , A61B5/0472 , A61B5/18 , A61B5/024 , A61B5/0468 , A61B5/046
摘要： Systems and methods for predicting and/or detecting cardiac events based on real-time biomedical signals are discussed herein. In various embodiments, a machine learning algorithm may be utilized to predict and/or detect one or more medical conditions based on obtained biomedical signals. For example, the systems and methods described herein may utilize ECG signals to predict and detect cardiac events. In various embodiments, patterns identified within a signal may be assigned letters (i.e., encoded as distributions of letters). Based on the known morphology of a signal, states within the signal may be identified based on the distribution of letters in the signal. When applied in the in-vehicle environment, drivers or passengers within the vehicle may be alerted when an individual within the vehicle is, or is about to, experience a cardiac event.
-
公开(公告)号：US11531851B2
公开(公告)日：2022-12-20
申请号：US16782573
申请日：2020-02-05
发明人： Kayvan Najarian , Jonathan Gryak , Elyas Sabeti , Joshua Drews
摘要： Computational algorithms integrate and analyze data to consider multiple interdependent, heterogeneous sources and forms of patient data, and using a classification model, provide new learning paradigms, including privileged learning and learning with uncertain clinical data, to determine patient status for conditions such as acute respiratory distress syndrome (ARDS) or non-ARDS.
-
公开(公告)号：US10936913B2
公开(公告)日：2021-03-02
申请号：US16357778
申请日：2019-03-19
发明人： Heming Yao , Kayvan Najarian , Jonathan Gryak , Wei Zhang
摘要： An automated pruning technique is proposed for reducing the size of a convolutional neural network. A large-sized network is trained and then connections between layers are explored to remove redundant parameters. Specifically, a scaling neural subnetwork is connected to the neural network and designed to infer importance of the filters in the neural network during training of the neural network. Output from the scaling neural subnetwork can then be used to remove filters from the neural network, thereby reducing the size of the convolutional neural network.
-
公开(公告)号：US11615527B2
公开(公告)日：2023-03-28
申请号：US16875357
申请日：2020-05-15
发明人： Kayvan Najarian , Heming Yao , Sayedmohammadreza Soroushmehr , Jonathan Gryak , Ryan W. Stidham
IPC分类号： G06K9/00 , G06T7/00 , G06K9/62 , G06T7/269 , G06T7/73 , G06T7/50 , G06N3/08 , G16H30/20 , G06F16/28
摘要： A system for automatically analyzing a video recording of a colonoscopy includes a processor and memory storing instructions, which when executed by the processor, cause the processor to receive the video recording of the colonoscopy performed on the colon and detect informative frames in the video recording. A frame is informative if the clarity of the frame is above a threshold or if the frame includes clinically relevant information about the colon. The instructions cause the processor to generate scores indicating severity levels of a disease for a plurality of the informative frames, estimate locations of the plurality of the informative frames in the colon, and generate an output indicating a distribution of the scores over one or more segments of the colon by combining the scores generated for the plurality of the informative frames and the estimated locations of the plurality of the informative frames in the colon.
-
公开(公告)号：US20210338171A1
公开(公告)日：2021-11-04
申请号：US17167140
申请日：2021-02-04
摘要： A method of generating an assessment of medical condition for a patient includes obtaining a patient data tensor indicative of a plurality of tests conducted on the patient, obtaining a set of tensor factors, each tensor factor of the set of tensor factors being indicative of a decomposition of training tensor data for the plurality of tests, the decomposition amplifying low rank structure of the training tensor data, determining a patient tensor factor for the patient based on the obtained patient data tensor and the obtained set of tensor factors, applying the determined patient tensor factor to a classifier such that the determined further tensor factor establishes a feature vector for the patient, the classifier being configured to process the feature vector to generate the assessment, and providing output data indicative of the assessment.
-
公开(公告)号：US20210022631A1
公开(公告)日：2021-01-28
申请号：US16554138
申请日：2019-08-28
发明人： Sayedmohammadreza Soroushmehr , Kayvan Najarian , Venkatakrishna Rajajee , Kevin Ward , Jonathan Gryak , Craig A. Williamson , Mohamad H. Tiba
摘要： A method of determining a diameter of a sheath of an optic nerve includes obtaining, by a processor, scan data representative of the optic nerve sheath, analyzing, by the processor, the scan data to find a position of a globe-optic nerve interface point, segmenting, by the processor, the scan data, processing, by the processor, the segmented scan data at an offset from the position of the globe-optic nerve interface point to determine boundary positions of the optic nerve sheath, and calculating, by the processor, the diameter of the optic nerve sheath based on the determined boundary positions.
-
公开(公告)号：US10463314B1
公开(公告)日：2019-11-05
申请号：US16040473
申请日：2018-07-19
申请人： Toyota Motor Engineering & Manufacturing North America, Inc. , THE REGENTS OF THE UNIVERSITY OF MICHIGAN
IPC分类号： G08B23/00 , A61B5/00 , A61B5/0456 , A61B5/0472 , A61B5/18 , A61B5/024 , A61B5/0468 , A61B5/046
摘要： Systems and methods for predicting and/or detecting cardiac events based on real-time biomedical signals are discussed herein. In various embodiments, a machine learning algorithm may be utilized to predict and/or detect one or more medical conditions based on obtained biomedical signals. For example, the systems and methods described herein may utilize ECG signals to predict and detect cardiac events. In various embodiments, patterns identified within a signal may be assigned letters (i.e., encoded as distributions of letters). Based on the known morphology of a signal, states within the signal may be identified based on the distribution of letters in the signal. When applied in the in-vehicle environment, drivers or passengers within the vehicle may be alerted when an individual within the vehicle is, or is about to, experience a cardiac event.
-
公开(公告)号：US11154254B2
公开(公告)日：2021-10-26
申请号：US16936011
申请日：2020-07-22
申请人： TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC. , THE REGENTS OF THE UNIVERSITY OF MICHIGAN
摘要： Systems and methods for predicting and/or detecting cardiac events based on real-time biomedical signals are discussed herein. In various embodiments, a machine learning algorithm may be utilized to predict and/or detect one or more medical conditions based on obtained biomedical signals. For example, the systems and methods described herein may utilize ECG signals to predict and detect cardiac events. In various embodiments, patterns identified within a signal may be assigned letters (i.e., encoded as distributions of letters). Based on the known morphology of a signal, states within the signal may be identified based on the distribution of letters in the signal. When applied in the in-vehicle environment, drivers or passengers within the vehicle may be alerted when an individual within the vehicle is, or is about to, experience a cardiac event.
-
9.发明申请公开(公告)号：US20200250496A1
公开(公告)日：2020-08-06
申请号：US16782573
申请日：2020-02-05
发明人： Kayvan Najarian , Jonathan Gryak , Elyas Sabeti , Joshua Drews
摘要： Computational algorithms integrate and analyze data to consider multiple interdependent, heterogeneous sources and forms of patient data, and using a classification model, provide new learning paradigms, including privileged learning and learning with uncertain clinical data, to determine patient status for conditions such as acute respiratory distress syndrome (ARDS) or non-ARDS.
-
公开(公告)号：US20200022658A1
公开(公告)日：2020-01-23
申请号：US16577380
申请日：2019-09-20
申请人： Toyota Motor Engineering & Manufacturing North America, Inc. , The Regents of the University of Michigan
IPC分类号： A61B5/00 , A61B5/0456
摘要： Systems and methods for predicting and/or detecting cardiac events based on real-time biomedical signals are discussed herein. In various embodiments, a machine learning algorithm may be utilized to predict and/or detect one or more medical conditions based on obtained biomedical signals. For example, the systems and methods described herein may utilize ECG signals to predict and detect cardiac events. In various embodiments, patterns identified within a signal may be assigned letters (i.e., encoded as distributions of letters). Based on the known morphology of a signal, states within the signal may be identified based on the distribution of letters in the signal. When applied in the in-vehicle environment, drivers or passengers within the vehicle may be alerted when an individual within the vehicle is, or is about to, experience a cardiac event.
-
-
-
-
-
-
-
-
-
搜索结果
10 条记录 (耗时 0.018 秒)
