AI Wound Boundary Recognition for 3D Bioprinting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for 3D bioprinting face challenges in accurately photographing and modeling wound boundaries, which is crucial for producing stable and functional living tissues, as existing techniques require manual input and lack efficient automated processes for recognizing wound boundaries in 3D images.
Innovation Solution
A method using artificial intelligence to automatically recognize wound boundaries in 3D images by calculating gradients and forming closed curves based on intersection points and variations, allowing for the generation of accurate 3D wound models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual input methods are used for wound boundary recognition, then accuracy can be maintained through expert judgment, but the process is time-consuming and lacks efficiency
Solution Approach 1:
The system enables automated self-service wound boundary recognition through AI algorithms that automatically process 3D image data, calculate gradients, and identify boundaries without requiring manual expert intervention, thereby improving efficiency while maintaining accuracy through algorithmic precision
Solution Approach 2:
The patent replaces manual mechanical input methods with an automated computational system that uses AI-based image processing, gradient calculation, and algorithmic boundary detection to automatically recognize wound boundaries from 3D images, eliminating the need for manual tracing while maintaining or improving accuracy
2Productivity
If automated recognition methods are implemented, then processing efficiency is improved, but accuracy may deteriorate due to lack of expert judgment
Solution Approach 1:
The patent replaces manual expert judgment with an automated AI-based image processing system that uses gradient calculation, point cloud analysis, and algorithmic boundary detection to automatically recognize wound boundaries, achieving both high speed and high accuracy through computational methods
Solution Approach 2:
The system changes the approach from subjective expert judgment to objective parameter-based analysis by calculating gradient values, analyzing point cloud distributions, and using quantifiable metrics to determine wound boundaries, thereby improving both automation and accuracy
3Manufacturing precision
If complex 3D modeling processes are used, then model accuracy is improved, but the device complexity and processing time increase
Solution Approach 1:
The patent extracts and focuses on the essential elements for accurate 3D modeling by specifically targeting wound boundary recognition through gradient calculation and point cloud analysis, separating the critical boundary detection process from other modeling steps to improve accuracy while managing complexity
Solution Approach 2:
The modeling process is segmented into distinct automated steps: image data acquisition, point cloud generation, gradient calculation at boundary points, boundary identification, and 3D model construction. This segmentation allows each step to be optimized independently, improving overall accuracy while making the complex process more manageable and efficient
Data Source
AI summary
The present specification discloses a method capable of automatically recognizing an accurate wound boundary and a method of generating a 3D wound model based on the recognized wound boundary. The method of automatically recognizing a wound boundary according to the present specification is a method of automatically recognizing a wound boundary based on artificial intelligence, and may photograph several frames of the wound to be recognized with an RGB-D camera, separating measurement information in an image, amplifying image data for learning, and passing the amplified image data through an artificial neural network. The method may include generating a three-dimensional (3D) model by performing boundary recognition post-processing on the data passing through the artificial neural network to match a two-dimensional (2D) image with the 3D model.