Automated Atrial Digital Twin Generation
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Solution Overview
Problem
Current methods for generating personalized computational models of the heart's atria are inefficient, require high human interaction, and are susceptible to user errors, lacking automation and reproducibility, especially when using non-invasive clinical data.
Innovation Solution
A highly automated method that preprocesses clinical input data to remove self-intersections and open anatomical openings, applies statistical shape models for anatomical fitting, and personalizes models through conduction velocity and fibrosis modeling, allowing for the generation of robust and reproducible atrial digital twins from various clinical data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection steps are used in CemrgApp and OpenEP for model generation, then user control and customization are improved, but automation extent and reproducibility deteriorate
Solution Approach 1:
The system performs automated model generation using clinical data as input, with the algorithm independently completing segmentation, mesh generation, and parameter optimization without requiring manual user selection steps. The model adapts automatically to different input data types (MRI, CT, electroanatomical maps) through self-adjusting processing pipelines.
Solution Approach 2:
Manual mechanical operations (user selection, manual segmentation, interactive model building) are replaced by an automated computational system that processes clinical data through algorithmic steps including image processing, mesh generation, and electrophysiological parameter optimization, eliminating the need for manual intervention while maintaining model quality.
2Adaptability or versatility
If manual selection steps are required in model generation processes, then flexibility in handling different data types is improved, but susceptibility to user errors and user-dependent deviations increases
Solution Approach 1:
The system is designed to accept multiple types of clinical input data (MRI, CT, electroanatomical maps) and automatically adapts the processing pipeline to handle each data type appropriately, producing consistent model quality across different input sources without requiring manual configuration or selection steps that could introduce user-dependent variations.
3Manufacturing precision
If high-level human interaction is required for building personalized computational models, then model accuracy and personalization are improved, but process efficiency and standardization deteriorate
Solution Approach 1:
The system incorporates automated feedback mechanisms where the computational model is continuously refined based on comparison with clinical data, automatically adjusting parameters and geometry to achieve high accuracy without manual intervention. The feedback loop validates model predictions against observed clinical measurements and iteratively optimizes the digital twin representation.
4Manufacturing precision
If current frameworks are used for integrating anatomical and functional twinning phases, then model personalization is improved, but computational efficiency and robustness deteriorate
Solution Approach 1:
The system merges the anatomical modeling phase and functional electrophysiological modeling phase into a unified automated workflow. Clinical imaging data is processed to generate anatomical geometry, which is then directly integrated with electrophysiological parameters and conduction velocity fields in a single cohesive computational model, eliminating the need for separate manual integration steps and reducing overall computational complexity.
Data Source
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AI summary
The invention relates to a method for generating an anatomically and functionally personalized computational model of a subject's heart (atria), i.e. a cardiac digital twin or virtual replica, to data processing devices and computer programs for carrying out the method and its use for carrying out in-silico experiments on the subject's cardiac digital twin, such as e.g. evaluation or prediction of cardiovascular treatment options or personalized therapy options.