CN-122005055-A - Systems and methods for improving cardiac ablation procedures

Abstract

Various systems and methods for improving cardiac ablation procedures are disclosed. The system and method include a cloud server including a database containing information about previously performed cardiac ablations, a local server communicatively coupled to the cloud server via a first network, and a surgical system communicatively coupled to the local server via a second network. The cloud server is configured to receive electrical data and anatomical data of a patient's heart from the surgical system via the local server, perform a comparison of the electrical data and the anatomical data of the heart with the database information, generate a surgical treatment plan for the heart based on the comparison, and transmit the surgical treatment plan to the surgical system via the local server, wherein the surgical treatment plan comprises a grid of the heart.

Inventors

• M. Amit
• B. Dilmoni

Assignees

• 韦伯斯特生物官能(以色列)有限公司

Dates

Publication Date

20260512

Application Date

20200506

Priority Date

20200501

Claims (9)

1. 1. A system for training a surgeon to perform cardiac ablation, the system comprising: A cloud server containing database information about previously performed cardiac ablations, and An AR/VR system communicatively coupled to the cloud server via a first network; Wherein the AR/VR system is configured to: an input defining parameters for training ablation is received, Receiving electrical data and anatomical data for a virtual patient from the cloud server, wherein the electrical data and the anatomical data for the virtual patient are determined by comparing the input to the database information, Creating a virtual simulation of the heart of the patient based on the electrical data and the anatomical data for the virtual patient, Measuring the performance of the trainee on the virtual simulation to perform a cardiac ablation procedure, and Scoring the performance of the trainee.
2. 2. The system of claim 1, wherein the cloud server receives the electrical data and the anatomical data of the heart previously subjected to cardiac ablation when performing a cardiac ablation procedure.
3. 3. The system of claim 1, wherein previously performed cardiac ablations comprise a plurality of ablations, wherein the cloud server receives the electrical data and the anatomical data of the heart of the patient after each ablation.
4. 4. The system of claim 1, wherein the cloud server is further configured to: Receiving additional electrical and anatomical data of the heart after performing the ablation procedure, and An ablation score is generated based on the additional electrical data and the anatomical data.
5. 5. A method for training a surgeon to perform cardiac ablation, the method comprising: receiving input defining parameters for training ablation; Receiving electrical data and anatomical data for a virtual patient from a cloud server, wherein the electrical data and the anatomical data for a virtual patient are determined by comparing the input to database information; creating a virtual simulation of the heart of the patient based on the electrical data and the anatomical data for the virtual patient; Measuring the performance of the trainee in performing a cardiac ablation procedure on the virtual simulation, and Scoring the performance of the trainee.
6. 6. The method of claim 5, further comprising receiving the electrical data and the anatomical data of the heart previously subjected to cardiac ablation while performing a cardiac ablation procedure.
7. 7. The method of claim 5, wherein when the previously performed cardiac ablation includes a plurality of ablations, the method further comprises receiving the electrical data and the anatomical data of the heart of the patient after each ablation.
8. 8. The method of claim 5, further comprising receiving additional electrical data and anatomical data of the heart after performing the ablation procedure.
9. 9. The method of claim 5, further comprising generating an ablation score based on the additional electrical data and the anatomical data.

Description

Systems and methods for improving cardiac ablation procedures Technical Field The present application provides systems, devices, and methods for improving cardiac ablation procedures. Background Cardiac ablation is a surgical procedure for treating an abnormal heart rhythm in a patient by cauterizing or destroying tissue in the patient's heart. Cardiac ablation is commonly used to treat cardiac arrhythmias such as atrial fibrillation, atrial flutter, supraventricular tachycardia, and Wolff-Parkinson-White syndrome. Cardiac ablation may prevent abnormal heart rhythms from moving through the heart. In a cardiac ablation procedure, electrical measurements of the heart are made using a catheter. Based on this measurement, the surgeon uses heat (radio frequency), extreme cold (cryoablation), or laser light to destroy the region of the heart where the electrical abnormality occurred. Traditional cardiac ablation procedures rely solely on the subjective skill of the surgeon to determine where and how to perform the ablation. This traditional approach results in high variability of clinical outcome, including high variability between patients, between surgeons, and between hospitals. Drawings A more detailed understanding of the following description may be had by way of example in conjunction with the accompanying drawings, in which: FIG. 1 is a diagram of an exemplary system in which one or more features of the present disclosure may be implemented; FIG. 2A illustrates a process of performing analysis of cardiac ablation data; FIG. 2B illustrates a process for generating a cardiac ablation treatment protocol in accordance with certain embodiments; FIG. 3 illustrates a process for performing cardiac ablation therapy in accordance with certain embodiments; FIG. 4 shows a graphical depiction of an artificial intelligence system; FIG. 5 illustrates a method performed in the artificial intelligence system of FIG. 4; fig. 6 shows an example of probability of naive bayes calculation; FIG. 7 illustrates an exemplary decision tree; FIG. 8 illustrates an exemplary random forest classifier; FIG. 9 illustrates an exemplary logistic regression; FIG. 10 illustrates an exemplary support vector machine; FIG. 11 illustrates an exemplary linear regression model; FIG. 12 illustrates an exemplary K-means clustering; FIG. 13 illustrates an exemplary ensemble learning algorithm; FIG. 14 illustrates an exemplary neural network; FIG. 15 illustrates a hardware-based neural network; FIG. 16 is a diagram of an exemplary system in which one or more features of the presently disclosed subject matter may be implemented; FIG. 17 illustrates a process for scoring cardiac ablation treatments according to some embodiments; FIG. 18 illustrates a process for training a surgeon to perform cardiac ablation using an AR/VR system in accordance with certain embodiments; FIG. 19A illustrates an example of a map that may be generated by a surgical system; FIG. 19B illustrates an example of a map that may be generated by a surgical system; FIG. 19C illustrates another example of a map that may be generated by a surgical system; FIG. 20A shows a display of the RF index Shure point; FIG. 20B illustrates a display identifying ablation points based on time; fig. 20C illustrates a display showing a stability view of the RF index detailed representation. Fig. 20D and 20E illustrate additional displays that provide displays of additional parameters to the using physician. FIG. 21A is a graphical representation of a cardiac ablation treatment regimen; FIG. 21B is a graphical representation of the location of a first ablation performed; FIG. 21C is a graphical representation of a first modified cardiac ablation treatment protocol; FIG. 21D is a graphical representation of the location of a second ablation; FIG. 21E is a graphical representation of a second modified cardiac ablation treatment protocol; FIG. 21F is a graphical representation of the location of a third ablation; FIG. 21G is a graphical representation of a third modified cardiac ablation treatment protocol, and Fig. 21H is a graphical representation of the location of a fourth ablation. Detailed Description The present invention exploits advances in machine learning and big data analysis to improve the clinical technique of cardiac ablation protocols. Embodiments of the present invention may use data collected from multiple patients, multiple surgeons, and multiple hospitals to generate cardiac ablation treatment protocols. Embodiments of the present invention further improve clinical outcome by providing systems and methods that dynamically adjust when performing cardiac ablation procedures. Embodiments of the present invention are able to achieve improved clinical results while also maintaining compliance with the U.S. health insurance privacy and liability Act (HIPAA) and European Union's general data protection Act (GDPR) in 1996. The present invention includes a system and method for improving car