3D Organ Fluid Modeling With ML-Based Flow Prediction
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Solution Overview
Problem
Conventional medical procedures involving biological structures rely on human interpretation of two-dimensional medical data, leading to costly and redundant analysis, and inefficiencies in determining the impact of fluids on these structures.
Innovation Solution
A system comprising a modeling component, a machine learning component, and a three-dimensional health assessment component that generates a three-dimensional model of a biological structure from multi-dimensional medical imaging data, predicts fluid flow and physics behavior, and renders physics modeling data, thereby providing a more accurate and efficient analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human interpretation of 2D medical data is used to determine fluid impact on biological structures, then analysis can be performed with simple equipment, but the process becomes burdensome, costly, and redundant
Solution Approach 1:
The patent transforms 2D medical imaging data into 3D models of biological structures, enabling comprehensive fluid dynamics analysis. This dimensional transition allows the system to capture complex anatomical geometries and fluid flow patterns that cannot be adequately represented in 2D, thereby improving analysis efficiency and accuracy while reducing human trial-and-error interpretation
Solution Approach 2:
The patent introduces a computational modeling system as an intermediary between raw medical imaging data and clinical decision-making. This intermediary automatically performs 3D reconstruction, mesh generation, and fluid dynamics simulations, replacing manual human interpretation and eliminating redundant analysis while managing system complexity through automated algorithms
2Measurement precision
If 3D modeling with fluid dynamics simulation is implemented, then accuracy of biological structure analysis is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary 3D reconstruction and mesh generation from medical imaging data before conducting fluid dynamics simulations. By pre-processing the anatomical data into optimized 3D models with appropriate mesh discretization, the system prepares the computational geometry in advance, enabling more efficient simulation execution and reducing overall processing time while maintaining high accuracy
Solution Approach 2:
The patent employs adaptive mesh refinement and adjustable simulation parameters to optimize computational efficiency. By dynamically adjusting mesh density in regions of high fluid flow gradients and using simplified geometries where appropriate, the system achieves accurate fluid dynamics results with reduced computational burden and shorter processing times
Data Source
AI summary
A multiple fluid model tool for multi-dimensional fluid modeling of a biological structure is presented. For example, a system includes a modeling component, a machine learning component, and a three-dimensional health assessment component. The modeling component generates a three-dimensional model of a biological structure based on multi-dimensional medical imaging data. The machine learning component predicts one or more characteristics of the biological structure based on input data and a machine learning process associated with the three-dimensional model. The three-dimensional health assessment component that provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the biological structure based on the input data and the one or more characteristics of the biological structure on the three-dimensional model.