Heartflow, Inc.

Paige.ai, Inc.

Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Siemens Corporation

The General Hospital Corporation

Siemens Healthcare Gmbh

Siemens Healthcare Diagnostics Gmbh

The Government of the United States of America As Represented by the Secretary of the Deptartment of Health and Human Services

University of Utah Research Foundation

Boston University

Beth Israel Deaconess Medical Center, Inc.

Leo John Grady

Charles Anthony Taylor, Jr

Michiel Schaap

Gilwoo Choi

Sethuraman Sankaran

Christopher Kanan

Thomas Fuchs

Christopher K Zarins

Timothy A Fonte

Souma Sengupta

Ying Bai

Belma Dogdas

Patricia Raciti

Jillian Sue

Nan Xiao

Stanley C Hunley

Campbell Rogers

Hyun Jin Kim

Michael Singer

Sophie Khem

Zhongle Wu

David Eberle

Peter Kersten Petersen

Supriya Kapur

Razik Yousfi

Rodrigo Ceballos Lentini

Sarah Wilkes

Jorge Sergio Reis-Filho

David Spain

Brandon Rothrock

Ryan Leonard Spilker

Matthew Houliston

Ran Godrich

Edward Karl Hahn, Iii

Clara Jaquet

Ali Kemal Sinop

Vivek Naresh Bhatia

David Lesage

Mark Rabbat

Noha Youssry El-Zehiry

Romain Moreau-Gobard

Shmuel Aharon

Rhea Tombropoulos

Jin Kim

Julian Viret

Travis Michael Sanders

Ali Kamen

Jonathan R Polimeni

Christopher Vincent Alvino

Alican Bozkurt

Peter Schueffler

Vivek K Goyal

Parmeshwar Khurd

Thomas Schiwietz

Matthew Lee

Daniel Weller

Nathalie D'Amours

Lawrence Leroy Wald

Marie-Pierre Jolly

Chenyang Xu

Gareth Funka-Lea

Navid Alemi

Dheeraj Singaraju

Jorge S Reis-filho

Jay Sastry

Dorin Comaniciu

Shaohua Kevin Zhou

Atilla Peter Kiraly

Michal Sofka

Berthold Kiefer

Eike Rietzel

John V Frangioni

Ali Khamene

Gianluca Paladini

Thomas J O'Donnell

Christian Tietjen

Martin Requardt

Thomas Boettger

Yiyong Sun

Ying Sun

Hari Sundar

Fred S Azar

Clifford R Weiss

Sajjad Hussain Baloch

Peter Gall

Michelle Xiaohong Yan

Lance Anthony Ladic

Ross Whitaker

Mamadou Diallo

Jason Locke

Daniel Cremers

Sara Good

Oliver Kutter

Tolga Tasdizen

Herve Jun Alfred Lombaert

Farida Cheriet

Herve Lombaert

Eleni Siampli

Systems and methods are disclosed for adjusting attributes of whole slide images, including stains therein. A portion of a whole slide image comprised of a plurality of pixels in a first color space and including one or more stains may be received as input. Based on an identified stain type of the stain(s), a machine-learned transformation associated with the stain type may be retrieved and applied to convert an identified subset of the pixels from the first to a second color space specific to the identified stain type. One or more attributes of the stain(s) may be adjusted in the second color space to generate a stain-adjusted subset of pixels, which are then converted back to the first color space using an inverse of the machine-learned transformation. A stain-adjusted portion of the whole slide image including at least the stain-adjusted subset of pixels may be provided as output.

Systems and methods to process electronic images to adjust attributes of the electronic images

Computer-implemented methods are disclosed for assessing the effect of musculoskeletal activities on disease and/or clinical events, the method comprising: receiving a patient-specific vascular and musculoskeletal model of a patient's anatomy, including at least one vessel of the patient; receiving at least one characteristic of the patient's musculoskeletal activity; generating or updating a computational anatomic vascular and musculoskeletal model of the patient's anatomy based on the received at least one characteristic of musculoskeletal activity; performing at least one of a computational fluid dynamics analysis or a structural mechanics simulation on the computational anatomic vascular and musculoskeletal model; and estimating at least one of the patient's risk of disease or clinical events based on the performed computational fluid dynamics analysis and/or structural mechanics simulation. Systems and computer readable media for executing these methods are also disclosed.

Systems and methods for cardiovascular blood flow and musculoskeletal modeling for predicting device failure or clinical events

Systems and methods are disclosed for simulation of occluded arteries and/or optimization of occlusion-based treatments. One method includes obtaining a patient-specific anatomic model of a patient's vasculature; obtaining an initial computational model of blood flow through the patient's vasculature based on the patient-specific anatomic model; obtaining a post-treatment computational model by modifying portions of the initial computational model based on an occlusion-based treatment; generating a pre-treatment blood flow characteristic using the initial computational model or computing a post-treatment blood flow using the post-treatment computational model; and outputting a representation of the pre-treatment blood flow characteristic or the post-treatment blood flow characteristic.

Systems and methods for simulation of occluded arteries and optimization of occlusion-based treatments

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient's vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient's vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.

Systems and methods of processing images to determine patient-specific plaque progression based on the processed images

Systems and methods are disclosed for creating an interactive tool for determining and displaying a functional relationship between a vascular network and an associated perfused tissue. One method includes receiving a patient-specific vascular model of a patient's anatomy, including at least one vessel of the patient; receiving a patient-specific tissue model, including a tissue region associated with the at least one vessel of the patient; receiving a selected area of the vascular model or a selected area of the tissue model; and generating a display of a region of the tissue model corresponding to the selected area of the vascular model or a display of a portion of the vascular model corresponding to the selected area of the tissue model, respectively.

Systems and methods for an interactive tool for determining and visualizing a functional relationship between a vascular network and perfused tissue

Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to identify a region of interest of the target specimen and determine an expression level of, category of, and/or presence of a biomarker in the region of interest, the biomarker comprising at least one from among an epithelial growth factor receptor (EGFR) biomarker and/or a DNA mismatch repair (MMR) deficiency biomarker, the machine learning system having been generated by processing a plurality of training images to predict whether a region of interest is present in the target electronic image, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the determined expression level of, category of, and/or presence of the biomarker in the region of interest.

Systems and methods for processing electronic images to infer biomarkers

Systems and methods are disclosed for using vessel reactivity to guide diagnosis or treatment for cardiovascular disease. One method includes receiving a patient-specific vascular model of a patient's anatomy, including at least one vessel of the patient; determining, by measurement or estimation, a first vessel size at one or more locations of a vessel of the patient-specific vascular model at a first physiological state; determining a second vessel size at the one or more locations of the vessel of the patient-specific vascular model at a second physiological state using a simulation or learned information; comparing the first vessel size to the corresponding second vessel size; and estimating a characteristic of the vessel of the patient-specific vascular model based on the comparison.

Systems and methods for vessel reactivity to guide diagnosis or treatment of cardiovascular disease

Systems and methods are disclosed for processing digital images to identify diagnostic tests, the method comprising receiving one or more digital images associated with a pathology specimen, determining a plurality of diagnostic tests, applying a machine learning system to the one or more digital images to identify any prerequisite conditions for each of the plurality of diagnostic tests to be applicable, the machine learning system having been trained by processing a plurality of training images, identifying, using the machine learning system, applicable diagnostic tests of the plurality of diagnostic tests based on the one or more digital images and the prerequisite conditions, and outputting the applicable diagnostic tests to a digital storage device and/or display.

Systems and methods to process electronic images to identify diagnostic tests

Systems and methods for treatment planning based on plaque progression and regression curves

Systems and methods are disclosed for processing digital images to predict at least one continuous value comprising receiving one or more digital medical images, determining whether the one or more digital medical images includes at least one salient region, upon determining that the one or more digital medical images includes the at least one salient region, predicting, by a trained machine learning system, at least one continuous value corresponding to the at least one salient region, and outputting the at least one continuous value to an electronic storage device and/or display.

Systems and methods to process electronic images for continuous biomarker prediction

Systems and methods are disclosed for diagnosis, risk assessment, and/or virtual treatment assessment of visceral ischemia and related disorders. One method includes receiving a patient-specific anatomic model of a patient's visceral vasculature, including visceral vasculature of the patient's visceral organs and bowel; determining a location in the patient-specific anatomic model of the patient's visceral vasculature; determining, for the location in the patient-specific anatomic model, a blood flow characteristic of blood flow through the location in the patient-specific anatomic model of the patient's visceral vasculature; determining a tissue region of the patient's bowel proximate the location in the patient-specific anatomic model of the patient's visceral vasculature; and generating an assessment of blood supply adequacy to the tissue region of the patient's bowel or generating a risk score for risk of disease for the patient's bowel, based on the determined blood flow characteristic and an expected blood flow characteristic associated with the tissue region of the patient's bowel.

Systems and methods for diagnosis, risk assessment, and/or virtual treatment assessment of visceral ischemia

Systems and methods are disclosed for determining blood flow characteristics of a patient. One method includes: receiving, in an electronic storage medium, patient-specific image data of at least a portion of vasculature of the patient having geometric features at one or more points; generating a patient-specific reduced order model from the received image data, the patient-specific reduced order model comprising estimates of impedance values and a simplification of the geometric features at the one or more points of the vasculature of the patient; creating a feature vector comprising the estimates of impedance values and geometric features for each of the one or more points of the patient-specific reduced order model; and determining blood flow characteristics at the one or more points of the patient-specific reduced order model using a machine learning algorithm trained to predict blood flow characteristics based on the created feature vectors at the one or more points.

Systems and methods for estimation of blood flow characteristics using reduced order model and/or machine learning

Systems and methods are disclosed for receiving digital images of a pathology specimen from a patient, the pathology specimen comprising tumor tissue, the one or more digital images being associated with data about a plurality of biomarkers in the tumor tissue and data about a surrounding invasive margin around the tumor tissue; identifying the tumor tissue and the surrounding invasive margin region to be analyzed for each of the one or more digital images; generating, using a machine learning model on the one or more digital images, at least one inference of a presence of the plurality of biomarkers in the tumor tissue and the surrounding invasive margin region; determining a spatial relationship of each of the plurality of biomarkers identified in the tumor tissue and the surrounding invasive margin region to themselves and to other cell types; and determining a prediction for a treatment outcome and/or at least one treatment recommendation for the patient.

Systems and methods for processing electronic images for biomarker localization

Systems and methods are disclosed for preserving patient privacy while allowing health data to be analyzed, managed, and stored in different geographical areas. One method for managing cross-border health data while preserving patient privacy includes: receiving a DICOM object from a hospital computing device for analysis; generating a unique case identifier for the DICOM object; validating the received DICOM object; if, based on the validation, the received DICOM object is valid, anonymizing the received DICOM object; updating the anonymous DICOM object to include the unique case identifier; compressing the updated DICOM object; and sending the compressed DICOM object to at least one data analysis web service(s).

Systems and methods for transmitting electronic data across networks

Systems and methods for processing images of slides to infer biomarkers

Growing community of inventors

Millbrae, CA, United States of America

Leo John Grady