Artery Width Detection Using Kernel-Based Ultrasound Pattern Recognition
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Solution Overview
Problem
Current methods for determining the diameter of an artery from ultrasound measurements require manual analysis by a professional operator, making them time-consuming and resource-intensive.
Innovation Solution
An automated method using a machine learning model to analyze time sequences of ultrasound data, identifying a region of interest within the data using kernels that represent the pattern of an artery, and determining the cross-sectional width of the artery based on this analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis by a professional operator is used to determine artery diameter from ultrasound measurements, then measurement precision is improved, but productivity deteriorates (time-consuming and resource-intensive)
Solution Approach 1:
The system performs automated self-analysis of ultrasound data using machine learning models and kernel-based pattern recognition, eliminating the need for manual operator intervention. The algorithm automatically identifies artery structures, determines cross-sectional width, and generates measurements independently, thereby resolving the contradiction between maintaining measurement precision and improving productivity.
2Measurement precision
If full ultrasound data is processed to determine artery diameter, then measurement precision is improved, but use of energy deteriorates (high processing resource requirements)
Solution Approach 1:
The system extracts only the relevant region of interest containing the artery from the full ultrasound dataset using kernel-based pattern recognition. By identifying and isolating the specific arterial structure through characteristic wall-lumen-wall patterns, the system processes only the necessary portion of the data, thereby maintaining measurement precision while significantly reducing energy consumption and processing resource requirements.
3Productivity
If automated determination is implemented, then productivity is improved, but device complexity deteriorates (requires machine learning models and processing resources)
Solution Approach 1:
The system employs lightweight, computationally efficient kernel-based algorithms that can be implemented in resource-constrained wearable devices. Instead of requiring complex deep learning models, the invention uses simplified pattern recognition approaches with pre-defined kernels that represent arterial wall-lumen-wall structures, enabling automated determination with minimal processing power and memory requirements.
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 method enables accurate and efficient determination of the cross-sectional width of an artery with low processing resource requirements, allowing for potential implementation in wearable devices for real-time monitoring.
Implementation Method 1
receiving a time sequence of sets of ultrasound data, each set representing reflection of ultrasound along a line extending through tissue including the artery
Data Source
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AI summary
A method for determining a cross-sectional width of an artery comprises: receiving (202) a time sequence of sets of ultrasound data, each set representing reflection of ultrasound along a line extending through tissue including the artery at a single point in time in the time sequence; and for each set of ultrasound data: determining (204), using a machine learning model (300), a region of interest within the ultrasound data, wherein the region of interest is determined using one or more kernels being compared to the ultrasound data, wherein each kernel represents a first wall portion at a first side of a cross-section of the artery, a second wall portion at a second side of the cross-section of the artery, and a lumen in-between; and determining (206) the cross-sectional width of the artery based on analysis of the region of interest of the ultrasound data.