Two-Stage Echocardiogram Validation for Accurate Cardiac Profiling
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Solution Overview
Problem
Current echocardiography methods rely heavily on human expertise, leading to inconsistencies and inefficiencies in imaging and analysis, making it difficult to achieve timely and accurate detection of cardiovascular diseases.
Innovation Solution
A smart ECHO system utilizing a two-stage machine-learning model that automates echocardiogram analysis, generating an initial cardiac profile and then refining it through validation calculations to provide a validated cardiac profile with improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual echocardiography analysis is performed by highly trained personnel, then accuracy and expertise are improved, but productivity and timeliness deteriorate due to time constraints and limited availability of experts
Solution Approach 1:
The patent replaces the manual mechanical analysis process performed by sonographers and echocardiographers with an automated computer-based system using machine learning models. The system automatically performs image analysis, measurements, and calculations, substituting human expertise with algorithmic processing to maintain accuracy while dramatically increasing throughput and eliminating time constraints associated with manual analysis.
Solution Approach 2:
The echocardiography system performs self-analysis through automated machine learning models that independently process images, extract features, and generate diagnostic information without requiring continuous human intervention. The system serves itself by automatically validating results and providing quantitative analytics, reducing dependency on highly trained personnel while maintaining or improving analysis quality.
2Productivity
If automated analysis systems are implemented, then productivity and timeliness are improved, but measurement precision and reliability may deteriorate without human expertise
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously learns from validated results and adjusts its analysis accordingly. The automated system includes validation loops that compare automated measurements against established criteria and provide feedback for refinement, ensuring reliability improves over time while maintaining high throughput capacity.
Solution Approach 2:
The system performs preliminary validation and quality checks automatically before final analysis completion. By pre-validating image quality, preprocessing data, and running initial assessments before the main analysis, the system ensures reliable results are produced efficiently without requiring post-processing intervention by experts.
3Measurement precision
If complex machine learning models are used, then analysis accuracy is improved, but device complexity and computational requirements worsen
Solution Approach 1:
The machine learning system is divided into multiple specialized models or stages, each handling specific aspects of echocardiogram analysis. This segmentation allows each component to be optimized for its specific function, improving overall accuracy while keeping individual model complexity manageable and enabling modular deployment that reduces system complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enhances the consistency and accuracy of echocardiography by providing near real-time, standardized image and data analytics, enabling precise grading of cardiovascular disease severity and complexity, even in the absence of highly trained personnel.
Implementation Method 1
The probe emits ultrasound waves that bounce off the heart structures and return as echoes
Implementation Method 2
The probe emits ultrasound waves that bounce off the heart structures and return as echoes
Data Source
AI summary
A smart echocardiography (ECHO) system includes a processor programmed to access a two-stage machine-learning (ML) model for analyzing echocardiograms. The two-stage ML model is trained to output a validated cardiac profile of a patient having improved accuracy based upon an inputted echocardiogram. The processor is further programmed to receive echocardiographic imaging data of a patient from the inputted echocardiogram and execute a first-stage of the two-stage ML model to generate an initial cardiac profile based on the echocardiographic imaging data. The initial cardiac profile includes a plurality of cardiac parameters each having a parameter value. The processor is further programmed to execute a second stage of the two-stage ML model on the initial cardiac profile by executing a plurality of validation calculations using the plurality of cardiac parameters and associated parameter values to generate a validated cardiac profile for the patient and output the validated cardiac profile.