3D Heart Valve Simulation for Post-Clip Outcome Prediction
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Solution Overview
Problem
Current approaches for predicting post-clip mitral valve gradient (MVG) and mitral regurgitation (MR) after mitral valve clip procedures are time-consuming and lack accuracy, while aortic valve repair techniques require expertise and are not widely applicable due to limited surgeon experience.
Innovation Solution
A generative computational predictive model that utilizes 3D imaging and parameterized heart valve simulations to predict post-operative outcomes, including MVG and MR, by segmenting and simulating surgical procedures on heart valves, allowing for pre-surgical planning and comparison of repair options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-scale patient-specific computational simulations are used to predict post-clip MVG and MR, then prediction accuracy is improved, but the procedure becomes time-consuming
Solution Approach 1:
The patent segments the complex computational simulation into distinct modules: patient-specific anatomical model creation from imaging data, surgical procedure simulation, and outcome prediction. This segmentation allows for optimized processing of each component, improving overall efficiency while maintaining accuracy.
Solution Approach 2:
The system performs preliminary actions by creating patient-specific anatomical models and simulating surgical procedures before actual surgery. This pre-operative virtual simulation allows surgeons to predict outcomes and plan procedures, reducing the need for time-consuming intraoperative adjustments while maintaining high prediction accuracy.
2Reliability
If aortic valve repair techniques are used, then long-term outcomes are improved by avoiding prosthetic valve complications, but the treatment is not widely applicable due to limited surgeon expertise
Solution Approach 1:
The patent creates virtual copies of patient-specific heart valve anatomy through 3D modeling from imaging data. These digital twins allow surgeons to practice and plan complex repair procedures virtually, making expert-level planning accessible without requiring every surgeon to have extensive hands-on experience with rare repair techniques, thereby improving treatment applicability while maintaining reliability.
Solution Approach 2:
The computational simulation system acts as an intermediary between surgeon expertise and patient-specific anatomical complexity. It translates complex surgical decision-making into visualizable outcomes, bridging the gap between limited surgeon experience and the need for personalized treatment planning, thus expanding treatment applicability while preserving long-term outcome reliability.
3Adaptability or versatility
If multiple repair options are evaluated, then treatment personalization is improved, but the decision-making process becomes more complex
Solution Approach 1:
The system provides visual feedback by displaying predicted surgical outcomes for different repair options side-by-side. This allows surgeons to compare effectiveness metrics and make informed decisions without navigating complex data presentations, thus maintaining treatment personalization while reducing decision-making complexity through intuitive visualization.
Data Source
AI summary
In certain aspects of the present disclosure, a computer-implemented method includes receiving a 3D imaging of a heart valve in a pre-operative state. The method includes generating a segmented heart valve by segmenting the heart valve of the 3D imaging. The method includes simulating a surgical procedure on the parameterized heart valve. The method includes determining at least one post-operative outcome based on simulating the surgical procedure on the parameterized heart valve. Systems and machine-readable media are also provided.