Electro-Anatomical Mapping From MRI and Electrograms Without Catheters
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Solution Overview
Problem
Traditional electro-anatomical mapping techniques require specialized facilities and invasive procedures, posing high costs and risks to patients, while lacking accessibility and comfort.
Innovation Solution
An apparatus and method using a processor and machine learning model to generate electro-anatomical mapping from medical images and electrograms, trained on historical data, providing a non-invasive and efficient clinical decision support system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electro-anatomical mapping techniques are used, then diagnostic accuracy is improved, but patient risk and discomfort increase due to invasive procedures
Solution Approach 1:
The patent replaces the mechanical invasive catheter-based mapping system with a non-invasive imaging and computational system. Specifically, it uses cardiac MRI imaging combined with machine learning algorithms to generate electro-anatomical maps without requiring physical catheter insertion into the heart, thereby eliminating the mechanical intrusion while preserving diagnostic capability
Solution Approach 2:
The patent creates a virtual copy or model of the heart's electrical anatomy through computational processing of MRI data. The machine learning model generates a digital electro-anatomical representation that mirrors the functional characteristics of the actual heart tissue, allowing diagnosis without direct physical measurement
2Measurement precision
If traditional electro-anatomical mapping techniques are used, then diagnostic precision is improved, but procedure cost increases
Solution Approach 1:
The patent makes the electro-anatomical mapping capability accessible through universal non-invasive imaging technology (cardiac MRI) that is more widely available than specialized electrophysiology labs. The machine learning model serves as a universal processing engine that can handle different patient datasets, making the diagnostic capability broadly accessible without requiring specialized facilities
Solution Approach 2:
The patent replaces expensive, specialized catheter-based mapping systems with a more cost-effective computational approach using standard cardiac MRI sequences. The machine learning model processes readily available imaging data to produce diagnostic-quality electro-anatomical maps at lower cost
3Loss of information
If traditional electro-anatomical mapping techniques are used, then electrical pathway information is obtained, but procedure complexity increases
Solution Approach 1:
The patent employs machine learning models that automatically process cardiac MRI data and generate electro-anatomical maps without requiring complex manual catheter manipulation or specialized procedural expertise. The system performs self-directed analysis of the imaging data, reducing the complexity burden on operators while preserving extraction of electrical pathway information
Data Source
AI summary
Apparatus for generating electro-anatomical mapping and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive input data, generate, using at least a machine learning model, an electro-anatomical mapping as a function of the input data, and display the electro-anatomical mapping using a user interface, wherein receiving the input data includes receiving, from an imaging device, at least a medical image and receiving, from a signal capturing device, at least an electrogram, wherein the at least a machine learning model is trained using electro-anatomical mapping training data including exemplary medical images and exemplary electrograms as input correlated to exemplary electro-anatomical mappings as output.