AVM Blood Flow Simulation for Rupture Risk Assessment
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Solution Overview
Problem
Current imaging techniques face challenges in accurately discerning individual blood vessels and determining blood flow characteristics in arterio-venous malformations (AVMs), leading to difficulties in assessing risk and planning effective treatments.
Innovation Solution
A computer-implemented method and system that utilize patient-specific models based on imaging data to analyze blood vessels associated with AVMs, perform blood flow simulations, and estimate risks of undesirable outcomes, evaluating treatment options such as embolization, ablation, or surgical removal by calculating stresses and predicting progression or regression of the AVM.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current imaging techniques are used to visualize AVMs, then the AVM can be detected, but individual blood vessels and blood flow characteristics cannot be accurately discerned
Solution Approach 1:
The patent segments the complex AVM vascular network into individual blood vessels and flow paths by processing medical images through a trained machine learning model. This segmentation enables separate analysis and measurement of each vessel's characteristics, resolving the inability to discern individual vessels within the tangled AVM structure.
Solution Approach 2:
The patent introduces a trained machine learning model as an intermediary between the raw medical images and the final analysis. This intermediary processes the images to extract vessel segmentation, flow directions, and characteristics, enabling accurate measurement of blood flow properties that were previously undetectable with standard imaging techniques.
2Reliability
If detailed analysis of individual blood vessels is performed, then accurate risk assessment can be achieved, but the complexity of the analysis process increases
Solution Approach 1:
The patent performs preliminary segmentation and identification of blood vessels and flow characteristics before the actual risk assessment. By pre-processing the medical images to extract vessel geometry, flow directions, and hemodynamic parameters, the system prepares structured data that simplifies the subsequent risk evaluation process while maintaining high accuracy.
Solution Approach 2:
The trained machine learning model automatically performs the complex tasks of vessel segmentation, flow characterization, and risk factor identification without requiring manual intervention. The system self-services by taking raw medical images as input and directly outputting structured risk assessments, reducing the need for complex manual analysis protocols.
3Manufacturing precision
If treatment options are evaluated using simulations, then treatment planning accuracy is improved, but the time required for evaluation increases
Solution Approach 1:
The patent performs preliminary extraction of vessel geometry, flow characteristics, and hemodynamic parameters from medical images before conducting treatment simulations. By pre-processing the data into structured formats with accurate vessel segmentations and flow measurements, the system reduces the computational burden during simulation phases, enabling faster evaluation of multiple treatment scenarios while maintaining precision.
Data Source
AI summary
A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.