AI Clinical Trial Image Standardization with Dual-Model Verification
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Solution Overview
Problem
The manual verification and standardization of clinical trial images across multiple institutions is time-consuming and labor-intensive, with repeated errors and corrections, and existing technologies lack efficient automated solutions for accurate standardization.
Innovation Solution
An automatic standardization device using artificial intelligence models, including a first model for rule-based labeling and a second model for image analysis, to extract and classify clinical trial image attributes, ensuring accurate standardization through a majority vote and data sufficiency checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification and standardization process is used for clinical trial images, then accuracy can be maintained through human review, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces the manual mechanical verification process with an automated AI-based system. The processor extracts attributes from DICOM headers and applies standardization rules automatically, eliminating the need for manual human review while maintaining standardization accuracy through programmed logic and algorithms.
Solution Approach 2:
The system enables self-service standardization by automatically processing clinical trial images through the processor, which extracts attributes, applies standardization rules, and generates standardized images without requiring external human intervention. The system serves itself by autonomously completing the entire standardization workflow.
2Reliability
If manual standardization process is used, then detailed verification can be performed, but manpower consumption increases
Solution Approach 1:
The patent substitutes manual operations with an automated processor that performs attribute extraction, rule application, and image standardization. This eliminates manpower consumption while maintaining reliability through consistent application of standardized rules and algorithms.
Solution Approach 2:
The system introduces an intermediary AI processing layer between the raw clinical trial images and the final standardized output. This intermediary processor automatically handles the standardization tasks, reducing the need for direct human involvement while ensuring reliable standardization through programmed logic.
3Productivity
If existing automated technologies are used for image standardization, then time consumption is reduced, but accuracy and reliability of standardization decrease
Solution Approach 1:
The patent segments the standardization process into distinct automated stages: attribute extraction from DICOM headers, classification based on extracted attributes, rule-based standardization application, and image generation. This segmented automated approach maintains high productivity while ensuring accuracy through systematic processing at each stage.
Solution Approach 2:
The system incorporates feedback mechanisms where the processor evaluates extracted attributes against predefined standardization rules, automatically adjusts processing parameters, and validates output consistency. This feedback loop ensures high standardization accuracy while maintaining automated efficiency.
Data Source
AI summary
Disclosed is an automatic standardization device for a clinical trial image, and the device extracts information for at least one attribute from medical standard data in the clinical trial image, and obtains a first output result based on the extracted information using the extracted information and a pre-learned first artificial intelligence model, obtains a second output result by analyzing the clinical trial image based on an image using a pre-learned second artificial intelligence model, and standardizes the clinical trial image based on the first output result and the second output result.