3D Biological Fluid Modeling for Accurate Organ Flow Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional analysis of biological structures using two-dimensional medical data is burdensome, requiring human interpretation and leading to trial and error, which is costly and redundant, and lacks accuracy in determining fluid impact on biological 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, minimizing human error and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human interpretation of 2D medical data is used to determine fluid impact, then analysis can be performed, but it results in human trial and error, burdening cost and time, and reducing accuracy
Solution Approach 1:
The patent transforms 2D medical imaging data into a 3D computational model of the biological structure, enabling multi-dimensional fluid modeling. This dimensional transition allows for more accurate representation of fluid flow patterns and their impact on biological structures, eliminating the limitations of 2D analysis while reducing reliance on time-consuming human interpretation
Solution Approach 2:
The patent replaces the mechanical human interpretation process with an automated computational system that performs fluid dynamics simulations. The system uses computer algorithms to analyze medical data, predict fluid flow behavior, and determine impact on biological structures, thereby eliminating human trial and error and significantly reducing analysis time
2Reliability
If human interpretation of 2D medical data is used, then analysis can be performed, but it leads to costly and redundant trial and error
Solution Approach 1:
The patent creates a universal computational platform that can handle multiple types of medical imaging data (CT, MRI, ultrasound) and apply various fluid dynamics models to different biological structures. This multi-functional system improves reliability by providing consistent, reproducible results across different cases while managing complexity through standardized processing workflows
Solution Approach 2:
The patent creates a virtual 3D computational copy of the biological structure from medical imaging data. This digital replica allows for repeated simulations and analysis without requiring physical intervention or redundant human interpretation, thereby improving reliability through consistent modeling while reducing the need for complex manual analysis procedures
3Measurement precision
If 2D medical data analysis is used, then fluid impact can be assessed, but it lacks accuracy in determining characteristics like blood viscosity and arterial strength
Solution Approach 1:
The patent utilizes 3D computational modeling to capture the spatial complexity of fluid flow patterns, vessel geometries, and tissue properties that cannot be adequately represented in 2D. This enables precise determination of medical characteristics such as blood viscosity and arterial strength by analyzing multi-dimensional flow dynamics and structural responses
Solution Approach 2:
The computational system automatically extracts and analyzes medical characteristics from the 3D model without requiring manual measurement or interpretation. The system self- computes fluid viscosity, arterial strength, and other physiological parameters through integrated algorithms, thereby maintaining ease of operation while dramatically improving measurement precision
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.