Ablation Contiguity Engine for Cardiac Gap Prediction

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Solution Overview

Problem

Conventional methods for wide area circumferential ablation (WACA) in cardiac treatments face limitations due to potential gaps between ablation points and dependence on anatomical structures, leading to pulmonary vein reconnection and recurrent arrhythmia, requiring improved methods for predicting gaps and anatomical segmentation.

Innovation Solution

An artificial intelligence and machine learning algorithm, the ablation contiguity engine, executes automatic contiguity estimations of WACA points using random forest regression, fully connected dense layers, and convolutional neural networks to provide real-time anatomical segmentation and gap prediction, reducing dependence on anatomical structures and improving treatment efficacy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual ablation site anatomical segmentation and manual WACA point contiguity estimation are used, then treatment accuracy can be maintained, but time consumption and operational complexity increase significantly

Engineering Contradiction:

Improvecontiguity estimation accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical assessment methods with an automated machine learning system. The ML model processes ablation point data, anatomical structures, and procedural parameters to automatically estimate contiguity and predict gaps, substituting the manual mechanical evaluation process while maintaining or improving accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing the ablation procedure to automatically assess its own contiguity and predict potential gaps without requiring external manual intervention. The ML model continuously monitors procedural parameters and provides real-time feedback, making the system self-evaluating and reducing dependency on operator expertise.

Inventive Principle:

Principle #25Self-service

2Productivity

If conventional WACA methods are used, then ablation points can be applied, but gaps between points may occur leading to pulmonary vein reconnection

Engineering Contradiction:

Improveablation efficiencyVSAvoidablation continuity

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent implements a feedback mechanism where the ML model continuously monitors ablation point placement, anatomical variations, and procedural parameters to predict potential gaps in real-time. This feedback loop allows operators to adjust the ablation strategy dynamically, ensuring complete circumferential isolation and preventing pulmonary vein reconnection.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary action by predicting potential gaps and identifying critical anatomical regions before ablation is completed. The ML model analyzes procedural data in advance to anticipate where gaps may form, allowing operators to proactively address these areas and ensure complete isolation before pulmonary vein reconnection can occur.

Inventive Principle:

Principle #10Preliminary action

3Extent of automation

If automated machine learning methods are implemented, then time efficiency and automation improve, but system complexity and computational requirements increase

Engineering Contradiction:

Improveautomation levelVSAvoidsystem complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent achieves universality by designing a multi-functional ML system that simultaneously performs anatomical segmentation, contiguity estimation, gap prediction, and procedural optimization. This single integrated system handles multiple tasks that would otherwise require separate tools, reducing overall system complexity despite the advanced capabilities.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Measurement precision

If manual review methods are used for contiguity estimation, then anatomical accuracy can be maintained, but operational dependence on anatomical structures increases

Engineering Contradiction:

Improveanatomical segmentation accuracyVSAvoidoperational simplicity

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The patent replaces manual mechanical assessment of anatomical structures with automated ML-based analysis. The system processes imaging data and anatomical parameters automatically, maintaining high segmentation accuracy while eliminating the need for operators to manually interpret complex anatomical relationships, thereby simplifying operation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20220005198A1Automatic contiguity estimation of wide area circumferential ablation points

Publication Date: 2022.01.06 BIOSENSE WEBSTER (ISRAEL) LTD

AI summary

A method is provided by an ablation contiguity engine executed by a processor. The method includes receiving at least wide area circumferential ablation points respective to tissue of an intra-body organ, estimating contiguity of the wide area circumferential ablation points with respect to locations to generate a contiguity estimation, and providing the contiguity estimation with the each of the wide area circumferential ablation points to support treatment of the tissue.