Machine learning system for assessing heart valves and surrounding cardiovascular tracts

• US 9,953,272 B2
• Filed: 04/26/2016
• Issued: 04/24/2018
• Est. Priority Date: 10/23/2013
• Status: Active Grant

• Current Assignee: Stenomics, Inc.
• Original Assignee: Stenomics, Inc.
• Inventors: Singer, Michael A.
• Primary Examiner(s): Holmes, Michael B
• Application Number:
  US15/138,922
• Publication Number:
  US 20160239764A1
• Time in Patent Office: 728 Days
• Field of Search: 706 12
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G06F 17/11 for solving equations , e.g. nonlinear equations, general mathematical optimization problems optimization specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A machine learning system for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof may include a training mode and a production mode. The training mode may be configured to train a computer and construct a transformation function to predict an unknown anatomical characteristic and/or an unknown physiological characteristic of a heart valve, inflow tract and/or outflow tract, using a known anatomical characteristic and/or a known physiological characteristic the heart valve, inflow tract and/or outflow tract. The production mode may be configured to use the transformation function to predict the unknown anatomical characteristic and/or the unknown physiological characteristic of the heart valve, inflow tract and/or outflow tract, based on the known anatomical characteristic and/or the known physiological characteristic of the heart valve, inflow tract and/or outflow tract.

Claims

1. A machine learning system including a computer for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof, the machine learning system comprising:

a transformation function configured to predict at least one of an unknown anatomical characteristic or an unknown physiological characteristic of at least one of a heart valve, an inflow tract or an outflow tract, using at least one of a known anatomical characteristic or a known physiological characteristic of the at least one heart valve, inflow tract or outflow tract; and

a production mode configured to use the transformation function to predict at least one of the unknown anatomical characteristic or the unknown physiological characteristic of the at least one heart valve, inflow tract or outflow tract, based on at least one of the known anatomical characteristic or the known physiological characteristic of the at least one heart valve, inflow tract or outflow tract, wherein the production mode is further configured to receive one or more feature vectors.

2. A machine learning system as in claim 1, wherein the machine learning system is configured to compute and store in a feature vector the at least one known anatomical characteristic or known physiological characteristic of the at least one heart valve, inflow tract or outflow tract.

3. A machine learning system as in claim 2, wherein the machine learning system is configured to calculate an approximate blood flow through the at least one heart valve, inflow tract or outflow tract.

4. A machine learning system as in claim 3, wherein the machine learning system is further configured to store quantities associated with the approximate blood flow through the at least one heart valve, inflow tract or outflow tract.

5. A machine learning system as in claim 4, wherein the machine learning system is further configured to perturb the at least one known anatomical characteristic or known physiological characteristic of the at least one heart valve, inflow tract or outflow tract stored in the feature vector.

6. A machine learning system as in claim 5, wherein the machine learning system is further configured to calculate a new approximate blood flow through the at least one heart valve, inflow tract or outflow tract with the perturbed at least one known anatomical characteristic or known physiological characteristic.

7. A machine learning system as in claim 6, wherein the machine learning system is further configured to store quantities associated with the new approximate blood flow through the perturbed at least one heart valve, inflow tract or outflow tract.

8. A machine learning system as in claim 7, wherein the machine learning system is further configured to repeat the perturbing, calculating and storing steps to create a set of feature vectors and quantity vectors and to generate the transformation function.

9. A machine learning system as in claim 1, wherein the production mode is configured to apply the transformation function to the feature vectors.

10. A machine learning system as in claim 9, wherein the production mode is configured to generate one or more quantities of interest.

11. A machine learning system as in claim 10, wherein the production mode is configured to store the quantities of interest.

12. A machine learning system as in claim 11, wherein the production mode is configured to process the quantities of interest to provide data for use in at least one of evaluation, diagnosis, prognosis, treatment or treatment planning related to a heart in which the heart valve resides.

13. A computer-implemented machine learning method for evaluating at least one characteristic of a heart valve, an inflow tract, an outflow tract or a combination thereof the method comprising:

predicting, with a transformation function on a computer, at least one of an unknown anatomical characteristic or an unknown physiological characteristic of at least one of a heart valve, an inflow tract or an outflow tract, using at least one of a known anatomical characteristic or a known physiological characteristic of the at least one heart valve, inflow tract or outflow tract;

maintaining, in a feature vector on the computer, the at least one known anatomical characteristic or known physiological characteristic of the at least one heart valve, inflow tract or outflow tract; and

using a production mode of a machine learning system on the computer to direct the transformation function to predict at least one of the unknown anatomical characteristic or the unknown physiological characteristic of the at least one heart valve, inflow tract or outflow tract, based on at least one of the known anatomical characteristic or the known physiological characteristic of the at least one heart valve, inflow tract or outflow tract.

14. A method as in claim 13, further comprising using the computer to calculate an approximate blood flow through the at least one heart valve, inflow tract or outflow tract.

15. A method as in claim 14, further comprising using the computer to store quantities associated with the approximate blood flow through the at least one heart valve, inflow tract or outflow tract.

16. A method as in claim 15, further comprising using the computer to perturb the at least one known anatomical characteristic or known physiological characteristic of the at least one heart valve, inflow tract or outflow tract stored in the feature vector.

17. A method as in claim 16, further comprising using the computer to calculate a new approximate blood flow through the at least one heart valve, inflow tract or outflow tract with the perturbed at least one known anatomical characteristic or known physiological characteristic.

18. A method as in claim 17, further comprising using the computer to store quantities associated with the new approximate blood flow through the perturbed at least one heart valve, inflow tract or outflow tract.

19. A method as in claim 18, further comprising using the computer to repeat the perturbing, calculating and storing steps to create a set of feature vectors and quantity vectors and to generate the transformation function.

20. A method as in claim 13, further comprising using the computer to perform the following steps:

receiving patient-specific data selected from the group consisting of anatomic data, physiologic data, and hemodynamic data;

generating a digital model of the at least one heart valve, inflow tract or outflow tract, based on the received data;

discretizing the digital model;

applying boundary conditions to at least one inflow portion and at least one outflow portion of the digital model; and

initializing and solving mathematical equations of blood flow through the digital model.

21. A method as in claim 20, further comprising storing quantities and parameters that characterize at least one of an anatomic state or a physiologic state of the digital model and the blood flow.

22. A method as in claim 21, further comprising perturbing at least one of an anatomic parameter or a physiologic parameter that characterizes the digital model.

23. A method as in claim 22, further comprising at least one of re-discretizing or re-solving the mathematical equations with the at least one anatomic parameter or physiologic parameter.

24. A method as in claim 23, further comprising storing quantities and parameters that characterize at least one of the anatomic state or the physiologic state of the perturbed model and blood flow.

25. A method as in claim 13, further comprising receiving one or more feature vectors with the production mode.

26. A method as in claim 25, further comprising using the production mode to apply the transformation function to the feature vectors.

27. A method as in claim 26, further comprising using the production mode to generate one or more quantities of interest.

28. A method as in claim 27, further comprising using the production mode to process the quantities of interest to provide data for use in at least one of evaluation, diagnosis, prognosis, treatment or treatment planning related to a heart in which the at least one heart valve, inflow tract or outflow tract resides.