Personalized Semi-Automatic Feature Analysis for Medical Imaging
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Solution Overview
Problem
Current medical imaging analysis systems lack flexibility in workflow, as they often rely on a one-size-fits-all approach for AI model predictions, which fails to accommodate user preferences and expertise levels, leading to suboptimal results and inefficient use of computational resources.
Innovation Solution
The system provides a personalized, semi-automatic feature analysis by allowing users to choose between feature segmentation and object detection and adjust AI model parameters, enabling user guidance at key stages of analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature segmentation is used for medical image analysis, then measurement precision is improved, but productivity deteriorates due to slower inference speed
Solution Approach 1:
The system dynamically switches between segmentation and object detection modes based on user selection and clinical context. The workflow allows users to choose segmentation for detailed analysis of specific regions while using object detection for broader screening, optimizing the balance between precision and speed for different diagnostic scenarios
Solution Approach 2:
The patent divides the medical image analysis into two distinct functional paths: segmentation for detailed feature extraction and object detection for rapid identification. This allows the system to apply the more computationally intensive segmentation method only when and where it is most beneficial, rather than applying it uniformly across all images
2Manufacturing precision
If feature segmentation is used, then manufacturing precision is improved, but loss of time increases due to longer annotation time
Solution Approach 1:
The system enables semi-automatic annotation where AI models perform the initial segmentation or detection, and users only need to review and refine the results when necessary. This self-service approach dramatically reduces the time required for high-precision annotation compared to manual methods
3Device complexity
If a one-size-fits-all AI model prediction approach is used, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system provides dynamic adaptability by allowing users to select between different AI model prediction approaches (segmentation vs. object detection) based on their expertise level, clinical task, and personal preference. This creates a flexible workflow that adapts to individual users rather than forcing a one-size-fits-all approach
Solution Approach 2:
The system incorporates multiple AI model types and prediction methods within a single unified platform, making it universally applicable to different user needs and clinical scenarios. Users can access both segmentation and object detection capabilities through the same interface, eliminating the need for separate specialized tools
4Measurement precision
If segmentation is applied to all features, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system applies different levels of analysis precision to different regions and features based on clinical relevance. Segmentation is applied only when high boundary precision is needed for specific features, while object detection suffices for other areas, optimizing energy usage by avoiding unnecessary computational overhead
Data Source
AI summary
The present disclosure is directed to systems and methods that address and improve upon certain technical challenges arising from the increasing use of artificial intelligence (AI) in medical imaging analysis. More specifically, the systems and methods described herein provide personalized, semi-automatic feature analysis that streamlines decision-making and workflow, simplifies the selection and use of available AI tools, and improves the functionality of these AI tools by enabling user input as key stages of the analysis.