Body-Part Image Scanner With Automatic Model Selection
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Solution Overview
Problem
Existing medical evaluation processes are inefficient and resource-intensive due to the need for manual model selection and integration of independent deep learning models, hindering fast and accurate health status assessments.
Innovation Solution
A scanning device and method that pre-classifies body-part images and automatically selects appropriate processing models for evaluation, integrating results for comprehensive health status reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple independent deep learning models are used for medical evaluation, then the accuracy of health status assessment is improved, but the complexity of model selection and integration increases
Solution Approach 1:
The patent combines multiple independent deep learning models into a unified evaluation system. The controller automatically selects and integrates results from multiple specialized models (e.g., tooth detection model, gum detection model, dental plaque detection model) to provide comprehensive health assessment, resolving the contradiction by merging model functions while maintaining accuracy.
Solution Approach 2:
The system implements automatic model selection and result integration without requiring manual intervention from medical personnel. The controller autonomously determines which models to apply based on the input image and automatically synthesizes their outputs, eliminating the burden of manual model selection while preserving the benefits of multiple specialized models.
2Measurement precision
If manual model selection and comprehensive analysis is performed by medical personnel, then accurate evaluation can be achieved, but the time consumption and resource burden increase
Solution Approach 1:
The system performs automatic model selection and result integration through the controller, eliminating the need for medical personnel to manually select models and synthesize results. This self-service approach maintains evaluation accuracy while dramatically reducing the time and cognitive resources required from medical professionals.
Solution Approach 2:
The system pre-configures multiple specialized deep learning models and their selection criteria in advance. When an image is input, the controller automatically applies the appropriate pre-prepared models and integrates their results, eliminating the need for real-time model selection and analysis by medical personnel.
3Productivity
If automated model selection and classification is implemented, then the efficiency of medical processes is improved, but the device complexity increases
Solution Approach 1:
The automated system is segmented into distinct functional modules: image classification module, model selection module, and result integration module. Each module performs a specific function, making the overall complex system manageable through modular design while maintaining high processing efficiency.
Data Source
AI summary
A health status evaluation method is provided. The method includes: receiving a body-part image; classifying the body-part image; selecting a processing model suitable for evaluating the body-part image; using the selected processing model to perform a health status evaluation on the body part corresponding to the body-part image; and integrating and outputting the health status evaluation results of the processing model.