Virtual Cardiac Catheterization From ECG and Echocardiogram Data
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Solution Overview
Problem
Existing cardiac catheterization procedures are invasive, costly, and require skilled physicians, while non-invasive methods lack accuracy and accessibility, limiting their effectiveness in diagnosing cardiovascular morbidity.
Innovation Solution
An apparatus and method using a processor and memory to generate cardiac catheterization data through machine learning, integrating electrocardiogram and echocardiogram data to predict catheterization parameters, providing standardized and quality-checked diagnostic feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cardiac catheterization is performed using invasive procedures, then diagnostic accuracy is improved, but procedure cost and complexity increase
Solution Approach 1:
The system creates a virtual copy of the invasive catheterization procedure by training machine learning models on datasets containing both non-invasive measurements and invasive catheterization results. The trained model then replicates catheterization functionality using only non-invasive inputs, eliminating the need for actual invasive procedures while maintaining diagnostic accuracy.
Solution Approach 2:
The patent replaces the mechanical invasive catheterization system with an information-processing system. Instead of physically inserting catheters into heart chambers, the system uses machine learning algorithms that process non-invasive medical data (ECG, echocardiogram, MRI) to predict catheterization parameters, substituting physical intrusion with computational analysis.
2Measurement precision
If cardiac catheterization is performed using invasive procedures, then diagnostic accuracy is improved, but procedure cost increases
Solution Approach 1:
The system creates a virtual copy of the invasive catheterization procedure by training machine learning models on datasets containing both non-invasive measurements and invasive catheterization results. The trained model then replicates catheterization functionality using only non-invasive inputs, eliminating the need for actual invasive procedures while maintaining diagnostic accuracy.
Solution Approach 2:
The patent employs inexpensive non-invasive data collection methods (standard ECG, echocardiogram, MRI) that replace expensive invasive catheterization procedures. These non-invasive tests are readily available, cost-effective, and can be performed frequently without the high costs associated with invasive procedures, including facility fees, specialized equipment, and physician time.
3Ease of operation
If non-invasive procedures are used for cardiac diagnosis, then accessibility is improved, but measurement accuracy deteriorates
Solution Approach 1:
The system creates a virtual copy of the invasive catheterization procedure by training machine learning models on datasets containing both non-invasive measurements and invasive catheterization results. The trained model then replicates catheterization functionality using only non-invasive inputs, eliminating the need for actual invasive procedures while maintaining diagnostic accuracy.
Solution Approach 2:
The patent transforms the relationship between non-invasive measurements and diagnostic accuracy by using machine learning to extract and analyze multiple parameters simultaneously from non-invasive data. The system processes complex patterns in ECG, echocardiogram, and MRI data to predict catheterization parameters with high accuracy, changing how non-invasive data is utilized from simple visual assessment to sophisticated computational analysis.
4Reliability
If invasive cardiac catheterization is performed, then reliable diagnostic data is obtained, but physician dependency increases
Solution Approach 1:
The system enables self-service by allowing the machine learning model to automatically analyze non-invasive medical data and generate diagnostic predictions without requiring physician interpretation. The algorithm independently processes ECG, echocardiogram, and MRI data to predict catheterization parameters, providing automated diagnostic support that reduces reliance on physician expertise while maintaining reliability.
Solution Approach 2:
The system creates a virtual copy of the invasive catheterization procedure by training machine learning models on datasets containing both non-invasive measurements and invasive catheterization results. The trained model then replicates catheterization functionality using only non-invasive inputs, eliminating the need for actual invasive procedures while maintaining diagnostic accuracy.
Data Source
AI summary
An apparatus and method for generating cardiac catheterization data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a plurality of cardiogram data examples; train a catheter data predictor using the plurality of cardiogram data examples; input a cardiogram data signal; generate a plurality of catheterization parameters from the cardiogram data signal and the catheter data predictor; and display the plurality of catheterization parameters.