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公开(公告)号:US20230214994A1
公开(公告)日:2023-07-06
申请号:US17569465
申请日:2022-01-05
Applicant: MERATIVE US L.P.
Inventor: Sun Young Park , Dustin Michael Sargent , Benedikt Graf , Larissa Christina Schudlo , Marwan Sati
CPC classification number: G06T7/0012 , G16H50/20 , G06F40/20 , G16H30/40 , G06T2207/20081
Abstract: Methods and systems for assigning a medical image study for review. One method includes receiving a plurality of labeled medical image studies and one or more prior image studies of a patient associated with each of plurality of labeled medical image studies. The method also includes creating a set of training data including the plurality of labeled medical image studies and the one or more prior image studies received for each of the plurality of labeled medical image studies and training an artificial intelligence (AI) system using the set of training data. In addition, the method includes estimating, using the AI system as trained, a difficulty metric for an unlabeled medical image study based on the unlabeled medical image study and one or more prior image studies of a patient associated with the unlabeled image study and assigning the unlabeled medical image study for review based on the difficulty metric.
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公开(公告)号:US11854197B2
公开(公告)日:2023-12-26
申请号:US17522196
申请日:2021-11-09
Applicant: Merative US L.P.
Inventor: Sun Young Park , Dustin Michael Sargent
CPC classification number: G06T7/0012 , A61B6/032 , A61B6/504 , A61B6/5217 , G06N3/04 , G06T7/70 , G06T2207/10081 , G06T2207/10116 , G06T2207/20081 , G06T2207/20084 , G06T2207/30101 , G06T2207/30196
Abstract: A computer system identifies a medical condition in a patient. A trained machine learning image generator is used to generate a set of training images based on three-dimensional patient imaging data, wherein each training image is labeled with a projection angle of the corresponding two-dimensional projection. Using the set of training images, a machine learning image classifier model is trained to identify patient rotation angles in x-ray images. X-ray images are processed with the machine learning image classifier model to identify patient rotation angles. A machine learning medical condition classifier model is trained to identify a medical condition using the labeled x-ray images. The machine learning medical condition classifier model determines an indication of the medical condition in a patient's x-ray image. Embodiments of the present invention further include a method and program product for identifying a medical condition in a patient in substantially the same manner described above.
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公开(公告)号:US20230154612A1
公开(公告)日:2023-05-18
申请号:US17530435
申请日:2021-11-18
Applicant: MERATIVE US L.P.
Inventor: Dustin Michael Sargent , Michael Trambert , Lenward E. Holness, JR. , Dale Seegmiller Maudlin , Sun Young Park
Abstract: A method and system is provided for radiology peer review feedback and learning using artificial intelligence. The system includes an electronic processor configured to: receive a set of medical imaging exams with reading physician data and at least one peer review score, train a machine learning algorithm to predict a review score from the medical imaging exam and reading physician data, use the trained machine learning algorithm to represent the medical imaging exam and the reading physician data, store a history of medical imaging exams for a reading physician, receive newly-reviewed medical imaging exam data for the reading physician having a feature vector, find similar medical imaging exams in the history of the medical imaging exams by comparing the feature vector of the newly-reviewed medical imaging exam data with the feature vectors for the medical imaging exams for the reading physician, and provide common review feedback to the reading physician.
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公开(公告)号:US20230154592A1
公开(公告)日:2023-05-18
申请号:US17530434
申请日:2021-11-18
Applicant: MERATIVE US L.P.
Inventor: Dustin Michael Sargent , Michael Trambert , Lenward E. Holness, JR. , Dale Seegmiller Maudlin , Sun Young Park
Abstract: A method and system is provided for optimizing radiology peer review exam selection using artificial intelligence. The system includes an electronic processor configured to: receive a set of candidate medical imaging exams with reading physician data, assign the medical imaging exams to at least one peer reviewer, receive peer reviewer data including scores and/or text for the assigned medical imaging exams, update a machine learning algorithm to optimize the assignment of medical imaging exams to the at least one peer reviewer using the received peer reviewer data.
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