Neural Network Training for ECG-Based LV Dysfunction Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for assessing left ventricular systolic and diastolic dysfunction (LVSD and LVDD) are laborious, expensive, and sometimes inaccurate, lacking a rapid and easily performed test for identifying cardiac disease.
Innovation Solution
An apparatus and method utilizing a neural network trained on electrocardiogram data to predict diastolic function and provide prognostic data, including classifications of normal LV function, LVSD, LVDD, and both, using a processor to analyze multi-channel sensor readings and generate diagnostic assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If echocardiograms are performed to assess left ventricular systolic and diastolic dysfunction, then diagnostic accuracy is improved, but the procedure becomes laborious, expensive, and less accessible
Solution Approach 1:
The patent creates a simplified copy of the echocardiogram assessment capability by training an AI model on echocardiogram data. The trained neural network then processes standard ECG data to reproduce the diagnostic functionality of echocardiograms, making the assessment accessible through routine ECG equipment rather than requiring complex echocardiography procedures
Solution Approach 2:
The patent replaces the mechanical and manual echocardiogram procedure with an automated AI-based system. The neural network automatically analyzes ECG data to detect left ventricular dysfunction, substituting the manual interpretation and complex procedural steps of echocardiograms with automated computational analysis of electrical signals
2Reliability
If echocardiograms are performed to assess cardiac disease, then diagnostic capability is improved, but cost and time requirements increase
Solution Approach 1:
The patent performs preliminary action by training the AI model in advance on comprehensive echocardiogram data. This pre-trained model can then rapidly assess new patients using only standard ECG data, eliminating the need to perform time-consuming echocardiograms at the point of care while maintaining diagnostic capability
Solution Approach 2:
The AI model creates a functional copy of echocardiogram diagnostic capability that can be executed rapidly on standard ECG equipment. This copy provides equivalent diagnostic information without requiring the time-intensive echocardiography procedure
3Ease of operation
If standard ECG analysis is used to assess cardiac function, then ease of operation is improved, but diagnostic precision for LVSD and LVDD is worsened
Solution Approach 1:
The patent applies parameter changes by transforming standard ECG parameters through the AI model to extract diagnostic information about left ventricular dysfunction. The neural network analyzes multiple ECG parameters simultaneously and transforms them into diagnostic predictions for LVSD and LVDD, enhancing the precision of routine ECG analysis without changing the ease of operation
Data Source
AI summary
An apparatus and method for training an artificial intelligence-supported diagnostic assessment tool may provide rapid and accurate prognosis determinations. Apparatus may include at least a processor configured to receive a plurality of multi-channel sensor readings of physiological data, generate training data correlating each of the plurality of multi-channel sensor readings with a plurality of diagnostic labels, train a neural network using the plurality of diagnostic labels, receive a time series input describing user physiological data from at least a sensor, input the time series input into the trained neural network, generate diagnostic data as a function of the time series input and the trained neural network, determine prognostic data as a function of the diagnostic data, and output the prognostic data.