Deep Learning Ablation Map Generation for Cardiac Arrhythmias
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Solution Overview
Problem
Current methods for identifying target ablation locations for treating cardiac arrhythmias, such as electro-anatomical maps and computational models, are invasive, computationally demanding, and lack real-time data integration, making them sub-optimal for persistent or permanent atrial fibrillation and ventricular tachycardia treatments.
Innovation Solution
A deep learning-based system that generates an ablation map using input medical images and voltage/activation maps, trained with a synthetic dataset simulating cardiac electrophysiology, to identify effective target ablation locations on the heart, thereby avoiding the limitations of conventional approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electro-anatomical maps are used to identify target ablation locations, then ablation targets can be identified, but the method is invasive and requires electrophysiology systems
Solution Approach 1:
The patent creates a virtual copy of the heart's electrical activity through computational models that simulate electrophysiology based non-invasively acquired imaging data (such as MRI or CT scans). This virtual model replicates the functionality of invasive electro-anatomical mapping by predicting voltage maps and identifying ablation targets without requiring actual catheter insertion or electrophysiology systems, thereby eliminating the harmful invasive aspect while preserving target identification accuracy
Solution Approach 2:
The patent replaces the mechanical invasive electrophysiology system with a computational modeling approach that uses non-invasive imaging data as input. Instead of physically inserting electrodes to map electrical activity, the system uses machine learning models to predict electrical behavior from structural imaging data, substituting a mechanical measurement system with an information-processing system that achieves the same diagnostic goal without physical intrusion
2Measurement precision
If computational models are used to simulate arrhythmias and identify ablation targets, then ablation targets can be identified, but the models are computationally demanding
Solution Approach 1:
The patent performs comprehensive computational simulations and model training in advance, creating pre-trained machine learning models that capture the complex relationships between imaging data and electrical behavior. Once trained, these models can rapidly predict ablation targets during clinical use without requiring repeated computationally intensive simulations, thereby shifting the computational burden to a preliminary offline phase and enabling fast real-time predictions during actual procedures
Solution Approach 2:
The patent transforms the computational approach by changing from direct physics-based simulations during clinical use to pre-trained machine learning model predictions. The system learns optimal parameters and relationships during training phase, then uses these learned parameters for rapid inference during clinical procedures, dramatically reducing computational resource requirements while maintaining prediction accuracy
3Loss of time
If computational modeling is performed prior to intervention, then ablation targets can be identified, but real-time data acquired during intervention cannot be leveraged
Solution Approach 1:
The patent creates a dynamic system where the pre-trained machine learning model can accept and process real-time data acquired during the intervention procedure. Instead of being a static pre-procedure tool, the model adapts to incorporate live measurements and imaging data, allowing it to update predictions and refine ablation target identification based on actual procedural findings, thereby making the system both time-efficient and information-complete
Data Source
AI summary
Systems and methods for generating an ablation map identifying target ablation locations on a heart of a patient are provided. One or more input medical images of a heart of a patient and a voltage map of the heart of the patient are received. An ablation map identifying target ablation locations on the heart is generated using one or more trained machine learning based models based on the one or more input medical images and the voltage map. The ablation map is output.