Cardiac Flow Detection With Valve Morphological Modeling
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Solution Overview
Problem
Accurate quantification of cardiac flow remains a challenge due to manual input variability and incomplete data capture in existing ultrasound imaging methods, leading to inaccuracies in cardiac flow measurement.
Innovation Solution
Automated detection of heart valves using machine-learned classifiers to initialize and track sampling planes, combined with B-mode and Doppler flow data for precise cardiac flow quantification, reducing user dependence and improving data completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual input methods are used to initialize sampling planes, then user control and flexibility are maintained, but measurement precision and reliability deteriorate due to user variability and incomplete data capture
Solution Approach 1:
The system performs automatic valve detection and sampling plane initialization without requiring manual user input. The machine learning model autonomously identifies cardiac valves and places sampling planes based on detected anatomical features, eliminating user variability while maintaining operational simplicity through automated self-service functionality
Solution Approach 2:
The manual mechanical process of visually locating and placing sampling planes is replaced by an automated machine learning system that uses image processing and pattern recognition to detect valves and initialize measurement planes, substituting human operator actions with algorithmic processing
2Reliability
If manual sampling plane placement is used, then device complexity is reduced, but measurement precision and data completeness worsen due to user error and incomplete anatomy capture
Solution Approach 1:
A machine learning model serves as an intermediary between the raw ultrasound images and the sampling plane placement process. This intermediary automatically detects cardiac valves and determines optimal sampling plane locations, improving reliability by removing human error while managing complexity through a specialized detection layer
Solution Approach 2:
The system performs preliminary automatic detection and initialization of sampling planes before flow measurement begins. By pre-identifying valve locations and placing sampling planes automatically, the system ensures complete anatomy capture and reliable measurement setup without requiring manual intervention during the measurement process
3Productivity
If automated machine learning detection is used, then measurement precision and productivity improve, but device complexity and computational requirements increase
Solution Approach 1:
The machine learning model is trained in advance on datasets of cardiac images, performing the complex learning task beforehand. During actual measurement, the pre-trained model rapidly detects valves and initializes sampling planes, achieving high productivity with reduced real-time computational burden
Solution Approach 2:
The system uses a pre-trained machine learning model that has learned from extensive training data. This copied knowledge from training datasets enables rapid, accurate detection during measurement without requiring the system to reprocess all training information, balancing complexity and productivity
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
Enables rapid, accurate quantification of cardiac flow parameters such as stroke volume, inflow, outflow, and regurgitant flow, supporting diagnosis and intervention decisions with reduced user input and enhanced precision.
Implementation Method 1
a transducer positioned on the chest of a patient such that acoustic energy passes between ribs of the patient to scan a heart or portion of the heart generating B-mode and Doppler flow data
Implementation Method 2
Doppler flow data
Data Source
AI summary
For cardiac flow detection in echocardiography, by detecting one or more valves, sampling planes or flow regions spaced from the valve and/or based on multiple valves are identified. A confidence of the detection may be used to indicate confidence of calculated quantities and/or to place the sampling planes.