Personalized Blood Flow Modeling Using Wearable Sensor Networks
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Solution Overview
Problem
Current methods for simulating blood flow in patients' vessels using medical images are limited by the inability to accurately account for individual patient states and are influenced by phenomena like white coat syndrome, leading to inaccurate measurements and correlations between central aortic and brachial artery blood pressures.
Innovation Solution
A system utilizing wearable sensor networks to continuously acquire cardiovascular measurements, allowing for the personalization of patient-specific blood flow models that can adapt to multiple patient states and correct for inaccuracies in clinical measurements, enabling the simulation of blood flow and pressure in patient-specific anatomical models to extract relevant hemodynamic measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous cardiovascular measurements are acquired using wearable sensor networks, then measurement precision and reliability of blood flow modeling are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system divides the cardiovascular monitoring task into multiple segments: wearable sensors acquire raw signals, a processor extracts cardiovascular parameters, and a blood flow model integrates multiple measurement sources. This segmentation allows continuous monitoring while managing complexity through modular architecture.
Solution Approach 2:
A computational blood flow model acts as an intermediary that integrates data from multiple wearable sensors and clinical measurements. This mediator synthesizes information from brachial artery pressure, pulse wave velocity, and other physiological parameters to produce accurate central aortic pressure estimates without requiring direct aortic measurement.
2Adaptability or versatility
If personalized blood flow models are created using patient-specific anatomical models and continuous measurements, then adaptability to individual patient states is improved, but computational requirements and model personalization complexity increase
Solution Approach 1:
Patient-specific anatomical models are constructed in advance from medical images before clinical use. These pre-built models incorporate individual anatomical features such as aortic geometry and vessel characteristics, allowing rapid personalization of blood flow simulations without requiring real-time complex computations during patient monitoring.
Solution Approach 2:
The blood flow model transitions from static anatomical structures to dynamic simulations that adapt to changing patient states. Continuous measurements from wearable sensors update model parameters in real-time, enabling the system to reflect physiological changes during exercise, stress, or disease progression while maintaining computational efficiency through reduced-order models.
3Measurement precision
If multiple measurement sources are integrated to correct for white coat syndrome and arterial wave reflections, then measurement precision is improved, but the complexity of data integration and processing increases
Solution Approach 1:
The system merges multiple measurement sources including brachial artery pressure from oscillometric cuffs, pulse wave velocity from tonometry, and continuous cardiovascular parameters from wearable sensors. By combining these diverse data streams through a unified blood flow model, the system compensates for measurement artifacts like white coat syndrome and arterial wave reflections that would affect single-source measurements.
Solution Approach 2:
The computational model implements feedback mechanisms where continuous wearable sensor measurements are fed back to update and refine blood pressure estimates in real-time. This closed-loop approach allows the system to continuously correct for physiological variations and measurement artifacts, improving accuracy of central aortic pressure estimation beyond what any single measurement method could achieve alone.
Data Source
AI summary
A method and system for personalized blood flow modeling based on wearable sensor networks is disclosed. A personalized anatomical model of vessels of a patient is generated based on initial patient data. Continuous cardiovascular measurements of the patient are received from a wearable sensor network on the patient. A computational blood flow model for simulating blood flow in the patient-specific anatomical model of the vessels of the patient is personalized based on the continuous cardiovascular measurements from the wearable sensor network. Blood flow and pressure in the patient-specific anatomical model of the vessels of the patient are simulated using the personalized computational blood flow model. Hemodynamic measures of interest for the patient are computed based on the simulated blood flow and pressure.