Machine Learning Collateral Circulation Score Computation
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Solution Overview
Problem
Conventional methods for assessing collateral coronary arteries are invasive, costly, time-consuming, and not suitable for routine clinical practice, lacking effective non-invasive and automated approaches to quantify collateral circulation for improved patient stratification and clinical decision-making.
Innovation Solution
A machine learning-based system that computes a collateral circulation score using patient data, including medical images, demographic, and lab/genetic information, employing trained networks for anatomical and functional assessments, and generating synthesized images to determine the functioning of collateral arteries, enabling automated and non-invasive evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional invasive pressure and velocity measurements are used to assess collateral arteries, then measurement precision is improved, but device complexity and ease of operation deteriorate due to invasiveness and procedural complexity
Solution Approach 1:
The patent replaces invasive mechanical pressure and velocity measurement systems with a non-invasive machine learning-based image analysis system. The system uses coronary angiography images as input and applies trained neural networks to automatically assess collateral artery function, eliminating the need for invasive catheter-based measurements while maintaining assessment accuracy.
Solution Approach 2:
The patent creates a virtual model of collateral artery function by training machine learning networks on synthesized and real angiography data. The trained model copies the assessment capabilities of invasive measurements by analyzing standard coronary angiography images, enabling non-invasive functional assessment that mirrors invasive measurement precision.
2Measurement precision
If manual annotations and observations are used for surrogate functional assessment, then measurement precision is improved, but productivity deteriorates due to time-consuming manual processes
Solution Approach 1:
The patent implements a self-service assessment system where the machine learning model automatically performs collateral artery evaluation without requiring manual annotations or observer expertise. The trained network processes coronary angiography images and generates functional assessments autonomously, eliminating time-consuming manual processes while maintaining measurement precision.
Solution Approach 2:
The patent replaces manual observation and annotation processes with automated machine learning-based image analysis. The system substitutes human expert time with computational algorithms that rapidly process angiography images to provide functional assessments, significantly improving productivity while preserving assessment accuracy.
3Ease of operation
If anatomical assessment by measuring artery size and length is performed, then ease of operation is improved, but measurement precision deteriorates as clinical outcome correlation is not confirmed
Solution Approach 1:
The patent transforms the assessment from simple anatomical parameters (size, length) to functional parameters (flow index, pressure index, resistance index, velocity flow reserve) using machine learning analysis of angiography images. This parameter transformation maintains ease of operation while significantly improving clinical outcome correlation by assessing actual collateral function rather than just anatomical presence.
Solution Approach 2:
The patent replaces simple anatomical measurement techniques with advanced machine learning-based functional assessment. The system analyzes coronary angiography images to compute functional indices that correlate with clinical outcomes, substituting basic morphological evaluation with sophisticated computational analysis that provides clinically relevant functional information.
Data Source
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AI summary
Systems and methods are provided for assessing collateral circulation of a patient. Patient data of a patient is received. A collateral circulation score is computed based on the patient data using a trained machine learning network. The collateral circulation score represents functioning of collateral circulation of the patient. The collateral circulation score is output.