Computational Heart Model for Arrhythmia Risk Stratification
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Solution Overview
Problem
Current risk stratification methods for ventricular arrhythmia in patients with repaired Tetralogy of Fallot lack consistent predictive value and clinical practicality due to the unique geometry and scar profile of TOF hearts, leading to unreliable identification of arrhythmia risk.
Innovation Solution
A computer-generated risk stratification method using contrast-agent-enhanced MRI data to construct three-dimensional models of the heart, simulating electromechanical function with multiple stimulation points, and classifying outcomes to provide a visual representation of electromagnetic activity, incorporating tissue types like normal, fibrotic, and scar tissue, and surgical patches to assess arrhythmia risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current risk stratification methods (surface ECG, Holter monitor, MRI metrics) are used, then clinical practicality is maintained, but predictive value and reliability are insufficient
Solution Approach 1:
The patent segments the heart into distinct tissue types (normal, fibrotic, scar, surgical patch) based on MRI contrast enhancement patterns. This segmentation allows for localized analysis of arrhythmia risk in specific regions, improving predictive value by identifying high-risk zones rather than providing a global assessment.
Solution Approach 2:
The patent assigns different electrical properties to different tissue types within the heart model. Fibrotic and scar tissues are assigned reduced electrical conductivity, while normal tissue maintains typical conductivity values. This local differentiation enables more accurate prediction of arrhythmia mechanisms and improves reliability of risk stratification.
2Measurement precision
If multiple electrical stimulations are applied at multiple stimulation points, then arrhythmia risk prediction accuracy is improved, but computational complexity and simulation time increase
Solution Approach 1:
The patent performs virtual electrical stimulations at multiple predetermined stimulation points (at least 9 in right ventricle and 17 in left ventricle) before clinical decision-making. By conducting these simulations in advance, the system identifies high-risk regions and arrhythmia mechanisms beforehand, improving prediction accuracy without delaying clinical intervention.
Solution Approach 2:
The patent creates a virtual copy of the patient's heart using image-based computational modeling. This digital twin allows for repeated electrical stimulations and arrhythmia inductions without physical risk to the patient. The virtual model can be stimulated multiple times with different protocols to assess arrhythmia risk comprehensively.
3Reliability
If image-based computational simulations are performed, then non-invasive arrhythmia risk assessment is achieved, but computational resources and processing requirements increase
Solution Approach 1:
The patent extracts only the essential features needed for arrhythmia risk assessment from the full MRI dataset. By focusing on tissue characterization through contrast enhancement patterns and creating simplified electrical property assignments, the system reduces computational burden while maintaining diagnostic accuracy for risk stratification.
Data Source
AI summary
An embodiment in accordance with the present invention provides a non-invasive solution to risk stratify the risk of in arrhythmia in patients with TOF. Currently, no reliable method for non-invasive risk stratification exists. In the realm of congenital heart disease, cardiac MRI is now used routinely for patients with Tetralogy of Fallot (TOF), the most common form of cyanotic congenital heart disease. An innovative platform for using clinical MRI data to create 3D electromechanical models of the heart enables predictions of whether or not patients with ischemic heart disease have the substrate for arrhythmia and what their relative risk for such an event is. An embodiment of the current invention provides a non-invasive solution to risk stratify the risk of arrhythmia in patients with TOF. Currently, no reliable method for non-invasive risk stratification exists.