Automated Cardiac Image Analysis System for Echocardiogram Metrics
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Solution Overview
Problem
Current medical imaging systems, such as echocardiograms, require extensive human interaction for setup, operation, and analysis, leading to inconsistencies and increased costs due to high inter-reader variability among clinicians, which can result in misdiagnosis and inefficient resource utilization.
Innovation Solution
A cardiac image analysis system utilizing a processor and memory to automatically analyze cardiac images by determining apical chamber image frames, tracing coordinates, and grid cell classifications, thereby calculating metrics like ejection fraction and left ventricular volume without human input, using trained models and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual marking of dimensions is performed by human clinicians, then clinical judgment and adaptability are maintained, but measurement precision and consistency deteriorate due to inter-reader variability
Solution Approach 1:
The patent replaces the manual mechanical process of human clinicians marking dimensions on echocardiograms with an automated computer vision system. The system uses machine learning models to automatically detect and mark anatomical structures and measure dimensions, eliminating inter-reader variability while maintaining measurement precision.
Solution Approach 2:
The system creates a digital copy of the echocardiogram images and processes this copy through automated algorithms. The machine learning model analyzes the digital image data to identify anatomical structures and perform measurements, replacing the need for human clinicians to manually mark dimensions on the original images.
2Reliability
If multiple clinicians analyze the same echocardiogram, then comprehensive clinical review is achieved, but time consumption and cost increase due to repeated manual analysis
Solution Approach 1:
The automated system performs self-service analysis of echocardiograms without requiring multiple clinicians to manually review each image. The machine learning model independently analyzes the echocardiogram data, identifies anatomical structures, and calculates measurements, providing consistent results across different patients and practitioners while significantly reducing analysis time.
3Adaptability or versatility
If a large number of image views are provided for comprehensive analysis, then diagnostic completeness is improved, but processing complexity and time required increase
Solution Approach 1:
The system segments the echocardiogram analysis into distinct functional components: image acquisition, anatomical structure identification, dimension measurement, and metric calculation. Each component is handled by specialized machine learning models, allowing comprehensive analysis of multiple views while managing processing complexity through modular architecture.
Solution Approach 2:
The machine learning system is designed with universal capabilities to handle multiple types of echocardiogram views and anatomical structures through a single integrated platform. The same system can analyze different image views (apical, parasternal, etc.) and calculate various cardiac metrics, providing comprehensive analysis without proportionally increasing processing complexity.
Data Source
AI summary
A system and method is provided for analyzing image data acquired from a patient. The method includes receiving image data associated with a patient, determining image frames with predetermined anatomical information from the cardiac image data, providing the image frames with the predetermined anatomical information to a trained model, and determining at least one of dimensional, volume, area, or physiological measurements using the trained model.