Multi-Model Arthritis Grading from X-Ray Local and Global Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional arthritis grade determination using artificial intelligence technology fails to reflect both optimistic and pessimistic medical staff determinations, as well as global and local analyses of X-ray images, leading to inconsistencies in diagnosis similar to actual medical site processes.
Innovation Solution
A method utilizing multiple artificial neural network models, including CNN-based and transformer-based models trained with different labeling schemes, to derive comprehensive arthritis grade information through preprocessing, local and global analysis, and optimistic/pessimistic determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a single artificial intelligence model is used for arthritis grade determination, then the diagnosis process is simplified and automated, but it cannot reflect both optimistic and pessimistic medical staff determinations or both global and local analyses
Solution Approach 1:
The patent divides the diagnosis system into multiple specialized models: CNN-based models for local analysis and transformer-based models for global analysis. Each model type focuses on specific aspects of X-ray interpretation, allowing the system to capture diverse medical reasoning patterns while maintaining automated operation.
Solution Approach 2:
The patent combines multiple AI models with different strengths (CNN and transformer architectures) into a unified diagnosis system. By integrating their outputs, the system achieves comprehensive analysis that reflects both optimistic and pessimistic medical staff determinations, resolving the contradiction between automation and precision.
2Measurement precision
If multiple artificial neural network models are used to reflect diverse medical staff determinations, then the diagnosis accuracy improves, but the system complexity increases
Solution Approach 1:
The patent designs a unified system architecture that handles multiple analysis types (local and global) and multiple determination perspectives (optimistic and pessimistic) through a consistent framework. This multi-functional design manages complexity by providing a universal interface for diverse model outputs.
Solution Approach 2:
The patent introduces an intermediary integration mechanism that combines outputs from multiple specialized models. This mediator layer manages the complexity of coordinating different model types while producing a unified diagnosis result, allowing high accuracy without direct exposure to the full system complexity.
3Ease of manufacture
If conventional single-model AI is used for arthritis diagnosis, then the implementation is straightforward, but it fails to implement the determination process similar to actual medical site processes
Solution Approach 1:
The patent assigns different analysis qualities to different model types: CNN models specialize in local feature extraction for detailed structural analysis, while transformer models handle global context and relationships. This local quality specialization allows the system to adapt to various medical determination approaches while maintaining ease of implementation through clear model roles.
Data Source
AI summary
Provided are a method, a system, and a computer-readable recording medium for determining an arthritis grade by using multiple artificial neural models, in which a first model, which corresponds to a CNN-based artificial neural network model, and a third model, which corresponds to a transformer-based artificial neural network model, are trained through training data corresponding to an X-ray image labeled in a first scheme in which the arthritis grade is labeled as being low, a second model, which corresponds to a CNN-based artificial neural network model, and a fourth model, which corresponds to a transformer-based artificial neural network model, are trained through the training data corresponding to the X-ray image labeled in a second scheme in which the arthritis grade is labeled as being high, and the arthritis grade for the X-ray image is determined by using the first model, the second model, the third model, and the fourth model.