Machine Learning Electro-Anatomical Mapping Apparatus
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Solution Overview
Problem
Traditional electro-anatomical mapping techniques are invasive, costly, and require specialized medical facilities, posing risks and discomfort to patients.
Innovation Solution
A processor-based apparatus that generates electro-anatomical mapping using machine learning models, integrating medical images and electrograms to create detailed, 3D representations of cardiac electrical pathways and structural features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electro-anatomical mapping techniques are used, then accurate diagnosis and treatment of cardiac arrhythmias is achieved, but patients experience invasive procedures, high costs, and significant risk
Solution Approach 1:
The patent creates a virtual copy of the electro-anatomical mapping process using machine learning models. Instead of physically inserting catheters into patients, the system uses training data from traditional mappings to generate synthetic electro-anatomical maps from non-invasive inputs like ECGs and medical images, thereby achieving accurate mapping without the harmful invasive procedure
Solution Approach 2:
The patent replaces the mechanical invasive catheter insertion system with a computational system. The machine learning model substitutes the physical mapping procedure with a digital processing pipeline that takes non-invasive signals and anatomical images as input, eliminating the need for mechanical intervention in the patient's body
2Measurement precision
If traditional electro-anatomical mapping techniques are used, then detailed 3D representation of conduction system is obtained, but specialized medical facilities and expensive equipment are required
Solution Approach 1:
The patent uses machine learning models trained on data from traditional electro-anatomical mapping systems to replicate the detailed 3D representation capability. The model learns the complex relationships between input data (ECGs, medical images) and output (electro-anatomical maps), allowing the system to generate detailed 3D representations without requiring the expensive specialized facilities needed for traditional methods
Solution Approach 2:
The patent changes the input parameters from direct physical measurements requiring specialized equipment to alternative data sources like standard ECGs and medical images. By transforming the input modality, the system achieves the same detailed 3D representation output using more accessible, less expensive input data
3Loss of information
If traditional electro-anatomical mapping techniques are used, then precise information about heart's electrical pathways is obtained, but patients endure significant discomfort and risk
Solution Approach 1:
The patent creates a virtual replica of the electro-anatomical mapping process that preserves the precision of electrical pathway information without the physical discomfort and risk. The machine learning model is trained to accurately reconstruct electro-anatomical maps from non-invasive inputs, maintaining information precision while eliminating the harmful procedural experience
Solution Approach 2:
The patent introduces machine learning models as an intermediary between the available non-invasive data (ECGs, medical images) and the desired electro-anatomical mapping information. This intermediary process extracts precise electrical pathway information indirectly, avoiding the need for direct invasive measurement and the associated discomfort and risk
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.