公开(公告)号：US11813113B2

公开(公告)日：2023-11-14

申请号：US17205485

申请日：2021-03-18

Applicant: Merative US L.P.

Inventor： Ehsan Dehghan Marvast ,  Allen Lu ,  Tanveer F. Syeda-Mahmood

IPC: G06T7/00 ,  G06T7/62 ,  A61B8/08 ,  A61B5/00 ,  G16H30/40 ,  A61B5/318 ,  A61B5/316 ,  G06V10/44 ,  G06F18/21 ,  G06F18/25 ,  G06V10/80 ,  G16H10/60

CPC classification number: A61B8/0883 ,  A61B5/316 ,  A61B5/318 ,  A61B5/72 ,  A61B8/5223 ,  G06F18/21 ,  G06F18/253 ,  G06T7/0012 ,  G06T7/0014 ,  G06T7/62 ,  G06V10/454 ,  G06V10/806 ,  G16H30/40 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30048 ,  G06V2201/031 ,  G16H10/60

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

公开(公告)号：US11645833B2

公开(公告)日：2023-05-09

申请号：US17528566

申请日：2021-11-17

Applicant: MERATIVE US L.P.

Inventor： Ali Madani ,  Mehdi Moradi ,  Tanveer F. Syeda-Mahmood

IPC: G16H30/40 ,  G16H30/20 ,  G06T7/00 ,  A61B6/00 ,  G06N5/02 ,  G06N20/00 ,  G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06N5/022 ,  G06N3/082

CPC classification number: G06K9/6259 ,  A61B6/5217 ,  G06K9/6267 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/0481 ,  G06N3/082 ,  G06N5/022 ,  G06N20/00 ,  G06T7/0014 ,  G16H30/20 ,  G16H30/40 ,  G06T2207/10116 ,  G06T2207/30048

Abstract: Mechanisms are provided to implement a machine learning training model. The machine learning training model trains an image generator of a generative adversarial network (GAN) to generate medical images approximating actual medical images. The machine learning training model augments a set of training medical images to include one or more generated medical images generated by the image generator of the GAN. The machine learning training model trains a machine learning model based on the augmented set of training medical images to identify anomalies in medical images. The trained machine learning model is applied to new medical image inputs to classify the medical images as having an anomaly or not.

公开(公告)号：US12272434B2

公开(公告)日：2025-04-08

申请号：US18221597

申请日：2023-07-13

Applicant: Merative US L.P.

Inventor： Tanveer F. Syeda-Mahmood ,  Chaitanya Shivade

IPC: G16H10/60 ,  G16H15/00 ,  G16H50/70 ,  G16H70/60

Abstract: Mechanisms are provided to implement a patient summary generation engine with deduplication of instances of medical concepts. The patient summary generation engine parses a patient electronic medical record (EMR) to extract a plurality of instances of a medical concept, at least two of which utilize different representations of the medical concept. The patient summary generation engine performs a similarity analysis between each of the instances of a medical concept to thereby calculate, for a plurality of combinations of instances of the medical concept, a similarity metric value. The patient summary generation engine clusters the instances of the medical concept based on the calculated similarity metric values for each combination of instances in the plurality of combinations of instances of the medical concept to thereby generate one or more clusters, and select a representative instance of the medical concept from each cluster in the one or more clusters. The patient summary generation engine generates a summary output of the patient EMR comprising the selected representative instances of the medical concept from each cluster.

公开(公告)号：US20230360751A1

公开(公告)日：2023-11-09

申请号：US18221597

申请日：2023-07-13

Applicant: Merative US L.P.

Inventor： Tanveer F. Syeda-Mahmood ,  Chaitanya Shivade

IPC: G16H10/60 ,  G16H50/70 ,  G16H70/60 ,  G16H15/00

CPC classification number: G16H10/60 ,  G16H50/70 ,  G16H70/60 ,  G16H15/00

Abstract: Mechanisms are provided to implement a patient summary generation engine with deduplication of instances of medical concepts. The patient summary generation engine parses a patient electronic medical record (EMR) to extract a plurality of instances of a medical concept, at least two of which utilize different representations of the medical concept. The patient summary generation engine performs a similarity analysis between each of the instances of a medical concept to thereby calculate, for a plurality of combinations of instances of the medical concept, a similarity metric value. The patient summary generation engine clusters the instances of the medical concept based on the calculated similarity metric values for each combination of instances in the plurality of combinations of instances of the medical concept to thereby generate one or more clusters, and select a representative instance of the medical concept from each cluster in the one or more clusters. The patient summary generation engine generates a summary output of the patient EMR comprising the selected representative instances of the medical concept from each cluster.

公开(公告)号：US11749387B2

公开(公告)日：2023-09-05

申请号：US17350441

申请日：2021-06-17

Applicant: Merative US L.P.

Inventor： Tanveer F. Syeda-Mahmood ,  Chaitanya Shivade

IPC: G16H10/60 ,  G16H50/70 ,  G16H70/60 ,  G16H15/00

CPC classification number: G16H10/60 ,  G16H15/00 ,  G16H50/70 ,  G16H70/60

Abstract: Mechanisms are provided to implement a patient summary generation engine with deduplication of instances of medical concepts. The patient summary generation engine parses a patient electronic medical record (EMR) to extract a plurality of instances of a medical concept, at least two of which utilize different representations of the medical concept. The patient summary generation engine performs a similarity analysis between each of the instances of a medical concept to thereby calculate, for a plurality of combinations of instances of the medical concept, a similarity metric value. The patient summary generation engine clusters the instances of the medical concept based on the calculated similarity metric values for each combination of instances in the plurality of combinations of instances of the medical concept to thereby generate one or more clusters, and select a representative instance of the medical concept from each cluster in the one or more clusters. The patient summary generation engine generates a summary output of the patient EMR comprising the selected representative instances of the medical concept from each cluster.