Patient-Specific Multi-Physics Heart Model for Cardiac Simulation
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Solution Overview
Problem
Current methods for diagnosing and treating heart failure are inadequate due to the complexity of simulating cardiac function and predicting therapy outcomes, as existing heart models lack personalization and accuracy in using medical image data and clinical measurements.
Innovation Solution
A multi-physics heart model is personalized using medical image data and clinical measurements to generate patient-specific computational models, integrating cardiac electrophysiology, biomechanics, and hemodynamics models, with parameters personalized using regression models and machine learning techniques to simulate cardiac function effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive cardiac models are used to simulate cardiac function, then diagnostic accuracy and therapy prediction improve, but model complexity and computational requirements increase
Solution Approach 1:
The comprehensive cardiac model is divided into three distinct sub-models: electrophysiology model, biomechanics model, and hemodynamics model. Each sub-model focuses on a specific aspect of cardiac function, allowing for specialized parameter personalization and independent optimization while maintaining overall system accuracy for diagnostic purposes.
Solution Approach 2:
The patent personalizes model parameters based on individual patient data including medical images, clinical measurements, and genetic information. By adjusting parameters such as tissue conductivity, muscle properties, and blood flow characteristics to match patient-specific data, the model achieves high diagnostic accuracy without requiring excessive structural complexity.
2Measurement precision
If patient-specific parameters are personalized using regression models, then model accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The patent employs pre-trained regression models that have been developed offline using extensive training datasets. These models capture the complex relationships between patient features and cardiac parameters in advance, allowing for rapid parameter personalization during clinical use without requiring real-time complex computations.
Solution Approach 2:
The patent uses machine learning models that learn from large datasets of patient-specific parameters and model outcomes. By training on comprehensive datasets beforehand, the system creates simplified computational representations that can quickly estimate parameters for new patients based on their specific data, balancing accuracy with processing speed.
Data Source
AI summary
A method and system for estimating physiological heart measurements from medical images and clinical data disclosed. A patient-specific anatomical model of the heart is generated from medical image data of the patient. A patient-specific multi-physics computational heart model is generated based on the patient-specific anatomical model by personalizing parameters of a cardiac electrophysiology model, a cardiac biomechanics model, and a cardiac hemodynamics model based on medical image data and clinical measurements of the patient. Cardiac function of the patient is simulated using the patient-specific multi-physics computational heart model. The parameters can be personalized by inverse problem algorithms based on forward model simulations or the parameters can be personalized using a machine-learning based statistical model.