Systems and methods for diagnostics for management of cardiovascular disease patients

Patent metadata

Field	Value
Publication number	US-12229957-B2
Application number	US-202318307316-A
Country	US
Kind code	B2
Filing date	Apr 26, 2023
Priority date	Aug 14, 2015
Publication date	Feb 18, 2025
Grant date	Feb 18, 2025

Abstract

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

First claim

The invention claimed is:

1. A system for implementing artificial intelligence, the system comprising:
a processor configured to:
receive imaging data of Computed Tomography (CT), Magnetic resonance (MR), Ultrasound (US), or Positron Emission Tomography (PET) of a patient;
determine, based on the received imaging date, a first set of quantities of anatomic descriptors, plaque descriptors or both as spatial distributions of biological properties, wherein the spatial distribution of biological properties are based on one or more transformations, wherein the one or more transformations include unwrapping the received imaging data;
determine, based on the first set of quantities, a second set of quantities that identify, characterize or both vascular disease burden as a medical condition; and
output the identification or characterization of the vascular disease burden.

2. The system of claim 1 wherein the quantities are lipid core, fibrosis, calcification, hemorrhage, permeability, thrombosis, ulceration, remodeling ratio, percent stenosis, percent dilation, wall thickness or any combination thereof.

3. The system of claim 1 wherein the processor is further configured to receive a patient's clinical history, a patient's symptoms, one or more diagnostic tests, or any combination thereof; and use any of the received patient's clinical history, received patient's symptoms, or received one or more diagnostic tests to determine the first set, the second set or both.

4. The system of claim 1 wherein the processor is further configured to receive one or more user inputs to adjust the first set, the second set, the vascular disease burden or any combination thereof.

5. The system of claim 1 wherein the processor is further configured to cause the one or more outputs to be transmitted to a display and, displayed using graphics and text.

6. The system of claim 1 wherein the processor utilizes one or more deep-learning algorithms.

7. The system of claim 1 wherein the second set of quantities, identify, characterize or both risk assessment or stratification of disease as a medical condition based on the first set of quantities.

8. The system of claim 1 wherein the second set of quantities, identify, characterize or both predicting a course of disease as a medical condition based on the first set of quantities, wherein the course of disease comprises adverse event outcomes.

9. The system of claim 1 wherein the second set of quantities, identify, characterize or both indicate a potential effectiveness of one treatment over another in individual patient based on the first set of quantities.

Assignees

• Elucid Bioimaging Inc

Inventors

• Buckler Andrew J
• Johnson Kjell
• Ma Xiaonan
• Moulton Keith A
• Paik David S

Classifications

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/096

  Transfer learning · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06T2207/30104

  Vascular flow; Blood flow; Perfusion · CPC title

• G06T2207/30096

  Tumor; Lesion · CPC title