Cardiac Device Digital Twin Hemodynamic Modeling
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Solution Overview
Problem
Current cardiovascular monitoring in anesthesia-resuscitation settings is insufficient for managing complex cases of hemodynamic instability, as traditional blood pressure and aortic blood velocity measurements do not fully capture the interactions between the heart and vessels.
Innovation Solution
A cardiac device that utilizes real-time digital cardiovascular modeling, combined with hemodynamic monitoring data, to continuously adapt and simulate indicators such as ventricular pressure/volume curves, vascular resistance, and myocardial constraints, potentially incorporating pharmacological inputs for predictive drug administration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hemodynamic monitoring (blood pressure and aortic blood velocity measurements) is used, then the monitoring system remains simple and easy to operate, but the measurement precision and completeness of cardiovascular status assessment is insufficient
Solution Approach 1:
A digital twin model of the cardiovascular system acts as an intermediary between traditional hemodynamic measurements and comprehensive cardiovascular assessment. The model receives simple inputs (blood pressure, aortic velocity) and transforms them into detailed virtual representations of cardiac function, including ventricular pressure-volume relationships and myocardial stress, without requiring direct complex measurements
Solution Approach 2:
The patent creates a digital copy (digital twin) of the patient's cardiovascular system that replicates its behavior and physiology. This virtual copy allows comprehensive assessment of cardiovascular status by simulating and analyzing parameters that would be difficult or impossible to measure directly in the physical system
2Loss of information
If numerical modeling with data assimilation is implemented, then inaccessible indicators (ventricular pressure/volume curves) become available, but the device complexity and computational requirements increase
Solution Approach 1:
The cardiovascular digital twin model is pre-configured with anatomical and physiological parameters before patient-specific data assimilation begins. This preliminary setup includes defining the ventricular geometry, tissue properties, and boundary conditions, allowing the model to quickly adapt to individual patients without requiring complex real-time calculations for basic model configuration
Solution Approach 2:
The system implements continuous feedback loops where measured hemodynamic data is assimilated into the digital twin model, which then predicts cardiovascular indicators. These predictions are continuously refined as new measurement data becomes available, creating a self-correcting system that improves accuracy over time while managing computational complexity through iterative refinement
3Measurement precision
If real-time digital cardiovascular modeling is used, then comprehensive and accurate real-time data is provided, but the computational processing requirements and system complexity increase
Solution Approach 1:
The system implements partial data assimilation by focusing computational resources on the most critical cardiovascular parameters and time points. Rather than continuously updating all model parameters at full resolution, the system selectively refines only those aspects of the digital twin that are most relevant to current clinical decision-making, reducing overall computational energy requirements
Data Source
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AI summary
A cardiac device comprises a memory (10) arranged for receiving haemodynamic data, and a computer (8) arranged for applying a cardiovascular model comprising a cardiac model and an arterial and venous blood circulation model using the data received in the memory (10), and for extracting therefrom at least one cardiac activity indicator (CI).