Ablation Gap Identification for Redo Pulmonary Vein Isolation

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Solution Overview

Problem

There is a need for a utility that helps physicians determine if a redo procedure for pulmonary vein isolation in atrial fibrillation is likely to have a positive outcome and decide upon a correct course of action while reducing risk to the patient.

Innovation Solution

An optimization engine utilizing machine learning and artificial intelligence to analyze data from previous and current ablation procedures, generating procedure expected outcomes and success predictions, and outputting ablation recommendations based on these predictions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a redo ablation procedure is performed to treat recurrent atrial fibrillation, then the likelihood of successful isolation may improve, but the patient risk and procedural complexity increase

Engineering Contradiction:

Improvesuccess rate of ablation procedureVSAvoidpatient risk

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The optimization engine performs preliminary analysis of ablation gap data before the redo procedure, identifying problematic areas in advance. This allows physicians to plan targeted ablation strategies that improve success rates while minimizing unnecessary tissue damage and patient risk.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates feedback from previous ablation outcomes and gap identifications to continuously improve procedure planning. By analyzing historical data and procedure results, the optimization engine refines its recommendations to enhance reliability while reducing risks in subsequent procedures.

Inventive Principle:

Principle #23Feedback

2Reliability

If comprehensive data analysis is performed to identify ablation gaps and predict outcomes, then procedure success rate improves, but computational complexity and data processing requirements increase

Engineering Contradiction:

Improveprocedure success prediction accuracyVSAvoidoptimization engine complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The optimization engine divides the complex data analysis task into separate modules: one for identifying ablation gaps from procedural data, another for predicting outcomes based on historical results, and a third for generating recommendations. This segmentation improves prediction accuracy while managing computational complexity through modular architecture.

Inventive Principle:

Principle #1Segmentation

3Measurement precision

If detailed performance metrics are collected and analyzed from mapping and ablation procedures, then identification of ablation gaps improves, but data processing time and computational resources increase

Engineering Contradiction:

Improveablation gap identification accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary processing and filtering of performance metrics during data collection, organizing information into structured formats before detailed analysis. This preliminary action reduces the computational burden during gap identification, maintaining high measurement precision while minimizing data processing time.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12514482B2Identification of ablation gaps

Publication Date: 2026.01.06 BIOSENSE WEBSTER (ISRAEL) LTD

AI summary

A method is described herein. The method is implemented by an optimization engine executed by a processor. The optimization engine receives data that includes performance metrics of mapping and ablation procedures. In turn, the optimization generates procedure expected outcomes for the mapping and ablation procedures based on the data and success predictions for a current ablation procedure utilizing the procedure expected outcomes. The optimization engine, also, outputs an ablation recommendation based on the success predictions.