Ian Paul Shadforth

US Patent:

20110077493, Mar 31, 2011

Filed:

Jun 30, 2010

Appl. No.:

12/826674

Inventors:

Ian SHADFORTH - San Francisco CA, US
David PRICE - Pleasanton CA, US
Gretchen ANDERSON - Oakland CA, US
Lorraine COMSTOCK - Saratoga CA, US
Mary McEVOY - Belmont CA, US
Douglas GRAHAM - Moray, GB
Alexander STRACHAN - Moray, GB
Alistair LONGMUIR - Forres, GB
Robert CAVAYE - Penarth, GB
Gillian TEFT - Maryburgh, GB

Assignee:

LifeScan Scotland Ltd. - Inverness-Shire

International Classification:

A61B 5/00

US Classification:

600365

Abstract:

Various embodiments are described and illustrated to calculate an insulin bolus, recommend such bolus, and provide reminder messages for performing an additional glucose test.

US Patent:

20100332142, Dec 30, 2010

Filed:

Jun 30, 2010

Appl. No.:

12/826670

Inventors:

Ian SHADFORTH - San Francisco CA, US
David PRICE - Pleasanton CA, US
Zara SIEH - Pleasanto CA, US
Brenda MONTGOMERY - Bellevue WA, US
Eric BERGMAN - Menlo Park CA, US

Assignee:

LifeScan,Inc. - Milpitas CA

International Classification:

G06F 19/00
A61B 5/00

US Classification:

702 19, 600300

Abstract:

Described herein are various methods to ensure safety and the compliance of therapeutic diabetes protocols. The method can be achieved by performing safeguards against hypoglycemia of the user prior to any change in basal insulin dosage based on the plurality of data.

US Patent:

20210369170, Dec 2, 2021

Filed:

Aug 16, 2021

Appl. No.:

17/402743

Inventors:

- Toronto, CA
Shyamlal Ramchandani - Kingston, CA
Timothy William Fawcett Burton - Toronto, CA
William Sanders - Bethseda MD, US
Ian Shadforth - Morrisville NC, US

International Classification:

A61B 5/316
A61B 5/00
A61B 5/02
A61B 5/282
A61B 5/026

Abstract:

The present disclosure facilitates the evaluation of wide-band phase gradient information of the heart tissue to assess, e.g., the presence of heart ischemic heart disease. Notably, the present disclosure provides an improved and efficient method to identify and risk stratify coronary stenosis of the heart using a high resolution and wide-band cardiac gradient obtained from the patient. The patient data are derived from the cardiac gradient waveforms across one or more leads, in some embodiments, resulting in high-dimensional data and long cardiac gradient records that exhibit complex nonlinear variability. Space-time analysis, via numeric wavelet operators, is used to study the morphology of the cardiac gradient data as a phase space dataset by extracting dynamical and geometrical properties from the phase space dataset.

US Patent:

20200205745, Jul 2, 2020

Filed:

Dec 23, 2019

Appl. No.:

16/725430

Inventors:

- Toronto, CA
Timothy William Fawcett Burton - Toronto, CA
Horace Gillins - Toronto, CA
Shyamlal Ramchandani - Kingston, CA
William Sanders - Bethesda MD, US
Ian Shadforth - Morrisville NC, US

International Classification:

A61B 5/00
G06N 3/08
G06N 20/00
G06K 9/62

Abstract:

The exemplified methods and systems facilitate the configuration and training of a neural network (e.g., a deep neural network, a convolutional neural network (CNN), etc.), or ensemble(s) thereof, with a biophysical signal data set to ascertain estimate for the presence or non-presence of disease or pathology in a subject as well as to assess and/or classify disease or pathology, including for example in some cases the severity of such disease or pathology, in a subject. In the context of the heart, the methods and systems described herein facilitate the configuration and training of a neural network, or ensemble(s) thereof, with a cardiac signal data set to ascertain estimate for the presence or non-presence of coronary artery disease or coronary pathology.

US Patent:

20200054232, Feb 20, 2020

Filed:

Jul 29, 2019

Appl. No.:

16/524475

Inventors:

- Toronto, CA
Shyamlal Ramchandani - Kingston, CA
Timothy William Fawcett Burton - Toronto, CA
William Sanders - Bethseda MD, US
Ian Shadforth - Morrisville NC, US

International Classification:

A61B 5/04
A61B 5/0408
A61B 5/026
A61B 5/02
A61B 5/00

Abstract:

The present disclosure facilitates the evaluation of wide-band phase gradient information of the heart tissue to assess, e.g., the presence of heart ischemic heart disease. Notably, the present disclosure provides an improved and efficient method to identify and risk stratify coronary stenosis of the heart using a high resolution and wide-band cardiac gradient obtained from the patient. The patient data are derived from the cardiac gradient waveforms across one or more leads, in some embodiments, resulting in high-dimensional data and long cardiac gradient records that exhibit complex nonlinear variability. Space-time analysis, via numeric wavelet operators, is used to study the morphology of the cardiac gradient data as a phase space dataset by extracting dynamical and geometrical properties from the phase space dataset.

US Patent:

20190365265, Dec 5, 2019

Filed:

Jun 3, 2019

Appl. No.:

16/429593

Inventors:

- Toronto, CA
Meng Lei - North York, CA
Ian Shadforth - Morrisville NC, US
Sunny Gupta - East York, CA
Timothy Burton - Toronto, US
Shyamlal Ramchandani - Kingston, CA

International Classification:

A61B 5/046
A61B 5/00
A61B 5/04
G06N 3/08
A61B 5/044

Abstract:

Phase space tomography methods and systems to facilitate the analysis and evaluation of complex, quasi-periodic system by generating computed phase-space tomographic images and mathematical features as a representation of the dynamics of the quasi-periodic cardiac systems. The computed phase-space tomographic images can be presented to a physician to assist in the assessment of presence or non-presence of disease. In some implementations, the phase space tomographic images are used as input to a trained neural network classifier configured to assess for presence or non-presence of pulmonary hypertension, including pulmonary arterial hypertension.

US Patent:

20190200893, Jul 4, 2019

Filed:

Dec 26, 2018

Appl. No.:

16/232586

Inventors:

- Toronto, CA
Meng Lei - North York, CA
Ian Shadforth - Morrisville NC, US
Sunny Gupta - Belleville, CA
Timothy William Fawcett Burton - Toronto, CA
Shyamial Ramchandani - Kingston, CA

International Classification:

A61B 5/053
A61B 5/04
A61B 5/00
A61B 5/044
G06T 11/00
G06T 17/20
G06T 3/40
G06T 11/60
A61B 5/026

Abstract:

The exemplified intrinsic phase space tomography methods and systems facilitate the analysis and evaluation of complex, quasi-periodic system by generating computed phase-space tomographic images as a representation of the dynamics of the quasi-periodic cardiac systems. The computed phase-space tomographic images can be presented to a physician to assist in the assessment of presence or non-presence of disease. In some embodiments, the phase space tomographic images are used as input to a trained neural network classifier configured to assess for presence or non-presence of significant coronary artery disease.

US Patent:

20190026431, Jan 24, 2019

Filed:

Jul 18, 2017

Appl. No.:

15/653441

Inventors:

- Kingston, CA
Timothy Burton - Ottowa, CA
Ali Khosousi - Toronto, CA
Abhinav Doomra - North York, CA
Sunny Gupta - Toronto, CA
Ian Shadforth - Morrisville NC, US

International Classification:

G06F 19/24
G01N 33/50
G06F 17/30
G06F 19/12

Abstract:

A facility for identifying combinations of feature and machine learning algorithm parameters, where each combination can be combined with one or more machine learning algorithms to train a model, is disclosed. The facility evaluates each genome based on the ability of a model trained using that genome and a machine learning algorithm to produce accurate results when applied to a validation data set by, for example, generating a fitness or validation score for the trained model and the corresponding genome used to train the model. Genomes that produce fitness scores that exceed a fitness threshold are selected for mutation, mutated, and the process is repeated. These trained models can then be applied to new data to generate predictions for the underlying subject matter.