AI-Rendered 3D Models from Medical Imaging for Patient Understanding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Patients struggle to understand their physiological parameters from medical images due to lack of knowledge and computational intensity in processing and rendering 3D ultrasound images, which is time-consuming and requires specific expertise.
Innovation Solution
A system utilizing an AI model to identify physiological parameters from medical imaging data, select a corresponding 3D model, and modify its parameters to create a customized, photo-realistic representation for patient understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If 3D ultrasound images are created from raw medical imaging data, then patient understanding of physiological parameters is improved, but computational intensity and processing time increase significantly
Solution Approach 1:
The patent creates a simplified 3D model representation that copies only the essential physiological parameter information from complex raw medical imaging data. This selective copying approach preserves the critical information needed for patient understanding while avoiding the computational burden of processing complete raw ultrasound volumes, thus reducing processing time while maintaining patient comprehension.
Solution Approach 2:
The system extracts and isolates specific physiological parameters from the complex medical imaging data, separating only the necessary information for patient visualization. By taking out only the essential parameters rather than processing the entire raw dataset, the system reduces computational intensity and processing time while still providing meaningful visual representations for patient understanding.
2Loss of information
If 3D ultrasound images are created from raw medical imaging data, then patient understanding of physiological parameters is improved, but device complexity and expertise requirements increase
Solution Approach 1:
The patent employs simplified 3D model representations that copy essential physiological parameter information without requiring complex 3D ultrasound reconstruction expertise. The system uses straightforward data acquisition and processing methods that can be performed by less experienced operators, reducing the expertise barrier while still delivering effective patient visualizations.
Solution Approach 2:
The system introduces an intermediary processing layer that translates complex raw medical imaging data into simplified 3D model representations. This intermediary step acts as a bridge, converting technically complex data into visually intuitive models that patients can understand, while the complexity is contained within the system rather than requiring operator expertise.
3Ease of operation
If raw medical imaging data is processed and rendered for patient display, then patient visualization capability is improved, but computational load increases
Solution Approach 1:
The system extracts only the essential physiological parameter information from the raw medical imaging data, separating and processing only what is necessary for patient visualization. By taking out and processing only the critical parameters rather than the entire dataset, the system reduces computational load while maintaining effective patient visualization capability.
Solution Approach 2:
The patent creates simplified 3D model copies that represent only the essential physiological parameters, avoiding the computational intensity of rendering complete raw medical imaging data. These simplified models provide adequate patient visualization while significantly reducing the computational energy required compared to processing the full raw dataset.
Data Source
AI summary
A method of creating a visual representation of an anatomical feature, wherein the method comprises deploying an AI model to execute on a computing device communicably connected to a medical imaging device, said medical imaging device acquiring medical imaging data, wherein the AI model is trained so that when it is deployed, the computing device identifies at least one type of anatomy from medical imaging data; acquiring, at the computing device, new medical imaging data; processing, using the AI model, the new medical imaging data to identify at least one type of anatomy; selecting a corresponding visual representation and modifying the corresponding visual representation to create a customized the visual representation.