公开(公告)号：US11877894B2

公开(公告)日：2024-01-23

申请号：US17219611

申请日：2021-03-31

Applicant: EKO.AI PTE. LTD.

Inventor： James Otis Hare, II ,  Su Ping Carolyn Lam ,  Yoran Hummel ,  Mathias Iversen ,  Andrie Ochtman

IPC: A61B8/00 ,  G06T7/00 ,  G06T7/10 ,  A61B8/08

CPC classification number: A61B8/466 ,  A61B8/463 ,  A61B8/468 ,  A61B8/469 ,  A61B8/488 ,  G06T7/0012 ,  G06T7/10 ,  G06T2207/10132 ,  G06T2207/20084 ,  G06T2207/30048

Abstract: Artificial intelligence (AI) recognition of echocardiogram (echo) images by a mobile ultrasound device comprises receiving a plurality of the echo images captured by the ultrasound device, the ultrasound device including a display and a user interface (UI) that displays the echo images to a user, the echo images comprising 2D images and Doppler modality images of a heart. One or more neural networks process the echo images to automatically classify the echo images by view type. The view type of the echo images is simultaneously displayed in the UI of the ultrasound device along with the echo images. A report is generated showing the calculated measurements of features in the echo images. The report showing the calculated measurements is displayed on a display device.

公开(公告)号：US20230351593A1

公开(公告)日：2023-11-02

申请号：US18316010

申请日：2023-05-11

Applicant: EKO.AI PTE. LTD.

Inventor： James Otis HARE, II ,  Su Ping Carolyn LAM ,  Yoran HUMMEL ,  Matthew FROST ,  Mathias IVERSEN ,  Cyril EQUILBEC ,  Zhubo JIANG

IPC: G06T7/00 ,  G06T7/11 ,  G06T7/90 ,  G06V20/40 ,  G06V10/764 ,  G06V10/82 ,  G16H30/40 ,  G16H70/20

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06T7/90 ,  G06V20/49 ,  G06V10/764 ,  G06V10/82 ,  G16H30/40 ,  G16H70/20 ,  G06T2207/30048 ,  G06T2207/10016 ,  G06V2201/031 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06T2207/10132 ,  G06T2207/30101

Abstract: A computer-implemented method for grading of Aortic Stenosis severity performed by an automated workflow engine executed by at least one processor includes receiving, from a memory, a plurality of echocardiogram images a heart. The plurality of echocardiogram (echo) images according to 2D images and Doppler modality images. The 2D images are classified by view type, and the Doppler modality images are classified by region. The regions of interest in the 2D images are segmented to produce segmented 2D images. The Doppler modality images are segmented to generate waveform traces to produce segmented Doppler modality images. The segmented images are used to calculate measurements of cardiac features of the heart. A conclusion of Aortic Stenosis severity is generated by comparing the calculated measurements to cardiac guidelines. A report is then output showing the calculated measurements of the cardiac features that fall within or outside of the cardiac guidelines.

公开(公告)号：US20200226757A1

公开(公告)日：2020-07-16

申请号：US16833001

申请日：2020-03-27

Applicant: EKO.AI PTE. LTD.

Inventor： James Otis HARE, II ,  Paul James SEEKINGS ,  Su Ping Carolyn LAM ,  Yoran HUMMEL ,  Jasper TROMP ,  Wouter OUWERKERK ,  Zhubo JIANG

IPC: G06T7/00 ,  G16H40/67 ,  G16H40/63 ,  G16H30/40 ,  G16H30/20 ,  G06T7/11 ,  G06N3/08 ,  G06N3/04

Abstract: A method for training neural networks of an automated workflow performed by a software component executing on a server in communication with remote computers at respective laboratories includes downloading and installing a client and a set of neural networks to a first remote computer of a first laboratory, the client accessing the echocardiogram image files of the first laboratory to train the set of neural networks and to upload a first trained set of neural networks to the server. The process continues until the client and the second trained set of neural networks is downloaded and installed to a last remote computer of a last laboratory, the client accessing the echocardiogram image files of the last laboratory to continue to train the second trained set of neural networks and to upload a final trained set of neural networks to the server.

公开(公告)号：US20200178940A1

公开(公告)日：2020-06-11

申请号：US16707622

申请日：2019-12-09

Applicant: Eko.AI PTE. LTD.

Inventor： James Otis HARE, II ,  Paul James SEEKINGS ,  Su Ping Carolyn LAM ,  Yoran HUMMEL ,  Jasper TROMP ,  Wouter OUWEKERK

IPC: A61B8/08 ,  G16H30/40 ,  G16H30/20 ,  A61B8/06

Abstract: An automated workflow performed by software executing on at least one processor includes receiving a plurality of echocardiogram images taken by an ultrasound device. A filter separates the plurality of echocardiogram images by 2D images and Doppler modality images based on analyzing image metadata. The 2D images are classified by view type, and the Doppler modality images are classified by view type. The cardiac chambers are segmented in the 2D images, and the Doppler modality images are segmented to generate waveform traces, producing segmented 2D images and segmented Doppler modality images. Using both the sets of images, measurements of cardiac features for both left and right sides of the heart are calculated. The calculated measurements are compared with international cardiac guidelines to generate conclusions and a report is output showing the calculated measurements that fall both within and outside of the guidelines.