AI-Supported ECG Diagnosis for Rapid LV Dysfunction Prognosis
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Solution Overview
Problem
Existing 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 cardiac disease evaluation.
Innovation Solution
An apparatus and method using artificial intelligence-supported diagnostic assessment tools that utilize a neural network trained on multi-channel sensor readings, including electrocardiogram data, to provide accurate prognostic data comparable to echocardiograms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If echocardiograms are used to assess left ventricular systolic and diastolic dysfunction, then diagnostic accuracy is improved, but the test becomes laborious, expensive, and less accessible
Solution Approach 1:
The patent replaces the mechanical echocardiography system with an AI-based diagnostic system that processes electrocardiogram data. The neural network model substitutes the need for complex echocardiogram equipment and operator skill, achieving comparable diagnostic accuracy through computational analysis of electrical heart signals.
Solution Approach 2:
The patent creates a computational model that copies the diagnostic functionality of echocardiograms using electrocardiogram data. The AI system learns to replicate echocardiogram-based assessments by training on paired datasets, enabling the simpler ECG test to provide equivalent diagnostic information about left ventricular dysfunction.
2Reliability
If echocardiograms are used for cardiac disease evaluation, then diagnostic capability is improved, but cost and time requirements increase
Solution Approach 1:
The patent performs preliminary training of the neural network model using extensive paired echocardiogram and ECG datasets before deployment. This preliminary action enables the system to quickly process new ECG data without requiring time-consuming echocardiogram procedures during actual patient assessment, achieving both accuracy and speed.
3Loss of information
If echocardiograms are used to assess LVSD and LVDD, then prognostic information is obtained, but accessibility and ease of performance deteriorate
Solution Approach 1:
The patent makes the electrocardiogram machine universal by enabling it to perform both traditional ECG analysis and AI-based assessment of left ventricular systolic and diastolic dysfunction. This multi-functionality allows a single, widely available device to provide comprehensive cardiac evaluation including prognostic information previously requiring specialized echocardiography.
Data Source
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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.