Estimators for ablation effectiveness

Patent metadata

Field	Value
Publication number	US-11622713-B2
Application number	US-201716349999-A
Country	US
Kind code	B2
Filing date	Nov 16, 2017
Priority date	Nov 16, 2016
Publication date	Apr 11, 2023
Grant date	Apr 11, 2023

Abstract

Methods for estimating of the effectiveness of catheter ablation procedures to form lesions. Lesion effectiveness parameters are received, and effectiveness of a corresponding ablation (optionally planned, current, and/or already performed) is estimated. The estimating is based on use by computer circuitry of an estimator constructed based on observed associations between previously analyzed lesion effectiveness parameters, and observed lesion effectiveness. The estimator is used by application to the received lesion effectiveness parameters.

First claim

What is claimed is:

1. A method of estimating effectiveness of ablation to form a lesion, the method comprising:
receiving lesion effectiveness parameters including parameters indicative of the conditions of formation and/or structure of the ablation lesion in the tissue and measurements of pre-lesioning condition of the tissue;
estimating an effectiveness of the ablation lesion in the tissue based upon applying received lesion effectiveness parameters to an estimator, wherein the estimator is constructed by computer circuitry and is based upon observed association between previously analyzed lesion effectiveness parameters and observed lesion effectiveness stored data; and
proceeding or completing an ablation treatment procedure of the tissue based upon an output of the estimation of the effectiveness of the ablation lesion.

2. The method of claim 1 , wherein the lesion effectiveness parameters further include patient parameter data and parameters of the ablation treatment procedure of the tissue.

3. The method of claim 2 , wherein said estimator is constructed by machine learning methods.

4. The method of claim 1 , wherein the pre-lesioning condition of the tissue is tissue thickness, fiber orientation, tissue types and/or dielectric measurements.

5. The method of claim 4 , wherein the pre-lesioning condition of the tissue is selected from the group consisting of tissue thickness, determined from dielectric measurements measured by an ablation catheter used to produce the lesion.

6. The method of claim 1 , wherein the conditions of formation of the ablation lesion is ablation power, dielectric quality of contact, angle of contact, force of contact and/or timing of ablation.

7. The method of claim 1 , wherein the structure of the ablation lesion is indicated by a measure of the depth, size and/or volume of the ablation lesion.

8. The method of claim 1 , wherein the step of receiving and estimating are performed during the ablation of the tissue.

9. The method of claim 1 , wherein the method further comprises adjusting a plan of ablation treatment based on the output of the estimation of the effectiveness of the ablation lesion.

10. The method of claim 1 , wherein the ablation lesion comprises a lesion formed by a single event of applying ablation energy to a focal region of the tissue.

11. The method of claim 1 , wherein the step of proceeding or completing the ablation of the tissue is based upon comparing the output of the estimation of the effectiveness of the ablation lesion with one or more criteria of effectiveness.

12. The method of claim 11 , wherein the one or more criteria of effectiveness comprises successful electrical isolation of electrical impulses.

13. A method of estimating effectiveness of ablation to form an ablation line, the method comprising:
receiving ablation line effectiveness parameters including parameters indicative of the conditions of formation and/or structure of the ablation line in the tissue and measurements of pre-lesioning condition of the tissue; and
estimating an effectiveness of the ablation line in the tissue based upon applying the received line effectiveness parameters to an estimator, wherein the estimator is constructed by a computer circuitry and is based upon observed association between previously analyzed ablation line effectiveness parameters and observed ablation line effectiveness stored data; and
proceeding or completing an ablation treatment procedure of the tissue based upon an output of the estimation of the effectiveness of the ablation line.

14. The method of claim 13 , wherein the ablation line effectiveness parameters further include patient parameter data and procedural parameters of the ablation treatment procedure of the tissue.

15. The method of claim 14 , wherein said estimator is constructed by machine learning methods.

16. The method of claim 13 , wherein the pre-lesioning condition of the tissue is selected from the group consisting of tissue thickness, fiber orientation, tissue types and/or dielectric measurements.

17. The method of claim 16 , wherein a lesion of the ablation line comprises a lesion formed by a single event of applying ablation energy to a focal region of the tissue.

18. The method of claim 13 , wherein the conditions of formation of the ablation line is selected from the group consisting of ablation power, dielectric quality of contact, angle of contact, force of contact and/or timing of ablation.

19. The method of claim 13 , wherein the structure of the ablation line is indicated by a measure of the depth, size and/or volume of a lesion of the ablation line.

20. The method of claim 13 , wherein the steps of receiving and estimating are performed during the ablation of the tissue.

21. The method of claim 13 , wherein the method further comprises adjusting a plan of ablation treatment based on the output of the estimation of the effectiveness of the ablation line.

22. The method of claim 13 , wherein the step of proceeding or completing the ablation of the tissue is based upon comparing the output of the estimation of the effectiveness of the ablation line with one or more criteria of effectiveness, wherein the one or more criteria of effectiveness comprises successful electrical isolation of electrical impulses across the ablation line.

23. A method of estimating effectiveness of ablation to form an ablation segment, the method comprising:
receiving ablation segment effectiveness parameters including parameters indicative of the conditions of formation and/or structure of the ablation segment in the tissue and measurements of pre-lesioning condition of the tissue; and
constructing an estimator, by computer circuitry, based upon observed associations between previously analyzed ablation segment effectiveness parameters and observed ablation segment effectiveness stored data;
estimating an effectiveness of the ablation segment in the tissue based upon applying the received segment effectiveness parameters to the estimator, wherein the estimator is constructed by a computer circuitry and is based upon observed association between previously analyzed ablation segment effectiveness parameters and observed ablation segment effectiveness stored data; and
proceeding or completing an ablation treatment procedure of the tissue based upon an output of the estimation of the effectiveness of the ablation segment.

24. The method of claim 23 , wherein the ablation segment effectiveness parameters further include patient parameter data and procedural parameters of the ablation treatment procedure of the tissue.

25. The method of claim 24 , wherein said estimator is constructed by machine learning methods.

26. The method of claim 23 , wherein the pre-lesioning condition of the tissue is selected from the group consisting of tissue thickness, fiber orientation, tissue types and/or dielectric measurements.

27. The method of claim 23 , wherein the conditions of formation of the ablation segment is selected from the group consisting of ablation power, dielectric quality of contact, angle of contact, force of contact and/or timing of ablation.

28. The method of claim 23 , wherein the structure of the ablation segment is indicated by a measure of the depth, size and/or volume of a lesion of the ablation segment.

29. The method of claim 23 , wherein the steps of receiving and

Assignees

• Navix Int Ltd

Inventors

• Ben-Haim Shlomo
• Shmayahu Yizhaq
• Schwartz Yitzhack
• Rodriguez Haim
• Dichterman Eli
• Ibragimov Zalman
• Yarden Yaara

Classifications

• A61B2090/062

  penetration depth · CPC title

• A61B2018/00779

  Power or energy · CPC title

• A61B18/12 Primary

  by passing a current through the tissue to be heated, e.g. high-frequency current · CPC title

• A61B34/25

  User interfaces for surgical systems · CPC title

• A61B5/4848 Primary

  Monitoring or testing the effects of treatment, e.g. of medication · CPC title