Jeremy M Howard

US Patent:

20200111561, Apr 9, 2020

Filed:

Dec 10, 2019

Appl. No.:

16/708906

Inventors:

- San Francisco CA, US
Devon Bernard - San Francisco CA, US
Li Yao - San Francisco CA, US
Ben Covington - Berkeley CA, US
Diogo Almeida - San Francisco CA, US
Brian Basham - San Francisco CA, US
Jeremy Howard - San Francisco CA, US
Anthony Upton - Malvern, AU
John Zedlewski - San Francisco CA, US

Assignee:

Enlitic, Inc. - San Francisco CA

International Classification:

G16H 30/40
G06F 3/048
G06T 7/00
G06Q 50/22
G16H 50/20
A61B 6/00
A61B 8/00
A61B 8/08
G16H 10/60
G16H 15/00
G16H 50/30
A61B 6/03
G06F 3/16
G06K 9/03
G06K 9/62
A61B 5/00
G06N 3/04
G06N 3/08
G16H 40/20
G06Q 10/10
G06T 7/11
G01T 1/24
H04N 5/32
G16H 40/63
G16H 50/70

Abstract:

A lung screening assessment system is operable to receive a chest computed tomography (CT) scan that includes a plurality of cross sectional images. Nodule classification data of the chest CT scan is generated by utilizing a computer vision model that is trained on a plurality of training chest CT scans to identify a nodule in the plurality of cross sectional images and determine an assessment score. A lung screening report that includes the assessment score of the nodule classification data is generated for display on a display device associated with a user of the lung screening assessment system.

US Patent:

20190279760, Sep 12, 2019

Filed:

May 23, 2019

Appl. No.:

16/420460

Inventors:

- San Francisco CA, US
Devon Bernard - San Francisco CA, US
Kevin Lyman - Fords NJ, US
Diogo Almeida - San Francisco CA, US
Jeremy Howard - San Francisco CA, US

Assignee:

Enlitic, Inc. - San Francisco CA

International Classification:

G16H 30/40
G06Q 50/22
G06F 17/27
A61B 6/00
H04N 5/32
A61B 5/00
G06T 7/00
G06Q 10/10
G01T 1/24
G16H 40/63
A61B 8/00
A61B 8/08
G06F 17/22
G06T 7/11
G06F 17/28
G16H 50/30
G06F 3/048
G16H 50/20
G16H 40/20
G06N 3/08
G06N 3/04
G06K 9/62
G06F 3/16
G06F 17/24
G06K 9/03
G16H 10/60
G16H 15/00

Abstract:

A medical scan image analysis system is operable to receive a plurality of medical scans that represent a three-dimensional anatomical region and include a plurality of cross-sectional image slices. A plurality of three-dimensional subregions corresponding to each of the plurality of medical scans are generated by selecting a proper subset of the plurality of cross-sectional image slices from each medical scan, and by further selecting a two-dimensional subregion from each proper subset of cross-sectional image slices. A learning algorithm is performed on the plurality of three-dimensional subregions to generate a fully convolutional neural network. Inference data corresponding to a new medical scan received via the network is generated by performing an inference algorithm on the new medical scan by utilizing the fully convolutional neural network. An inferred abnormality is identified in the new medical scan based on the inference data.

US Patent:

20180338741, Nov 29, 2018

Filed:

Aug 30, 2017

Appl. No.:

15/690786

Inventors:

- San Francisco CA, US
Devon Bernard - San Francisco CA, US
Li Yao - San Francisco CA, US
Ben Covington - Berkeley CA, US
Diogo Almeida - San Francisco CA, US
Brian Basham - San Francisco CA, US
Jeremy Howard - San Francisco CA, US
Anthony Upton - Malvern, AU
John Zedlewski - San Francisco CA, US

Assignee:

Enlitic, Inc. - San Francisco CA

International Classification:

A61B 6/00
A61B 6/03

Abstract:

A lung screening assessment system is operable to receive a chest computed tomography (CT) scan that includes a plurality of cross sectional images. Nodule classification data of the chest CT scan is generated by utilizing a computer vision model that is trained on a plurality of training chest CT scans to identify a nodule in the plurality of cross sectional images and determine an assessment score. A lung screening report that includes the assessment score of the nodule classification data is generated for display on a display device associated with a user of the lung screening assessment system.

US Patent:

20180342060, Nov 29, 2018

Filed:

Aug 30, 2017

Appl. No.:

15/690752

Inventors:

- San Francisco CA, US
Devon Bernard - San Francisco CA, US
Kevin Lyman - Fords NJ, US
Diogo Almeida - San Francisco CA, US
Jeremy Howard - San Francisco CA, US

Assignee:

Enlitic, Inc. - San Francisco CA

International Classification:

G06T 7/00
G06T 7/11

Abstract:

A medical scan image analysis system is operable to receive a plurality of medical scans that represent a three-dimensional anatomical region and include a plurality of cross-sectional image slices. A plurality of three-dimensional subregions corresponding to each of the plurality of medical scans are generated by selecting a proper subset of the plurality of cross-sectional image slices from each medical scan, and by further selecting a two-dimensional subregion from each proper subset of cross-sectional image slices. A learning algorithm is performed on the plurality of three-dimensional subregions to generate a fully convolutional neural network. Inference data corresponding to a new medical scan received via the network is generated by performing an inference algorithm on the new medical scan by utilizing the fully convolutional neural network. An inferred abnormality is identified in the new medical scan based on the inference data.