Image-Centered Clinical Trial Analysis Platform
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing clinical trial images are inefficient due to application-centered approaches, leading to mismatches between images and analysis software, and result in discrepancies across different vendor platforms, with no standardized way to minimize these differences.
Innovation Solution
A system and method for quantitative image analysis that organizes applications in an image-centered manner, using a trial specification unit to encode and communicate protocols, selecting appropriate software applications, and generating compensation data to refine analysis results, ensuring consistency across different platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If application-centered analysis methods are used, then users can access dedicated analysis tools for specific clinical trials, but it becomes inefficient for users to locate the right application and load the right image, and images and applications can be mismatched
Solution Approach 1:
The patent inverts the traditional application-centered approach by implementing an image-centered system. Instead of users searching for applications and loading images, the system automatically reads image metadata, identifies the appropriate analysis protocol and application, and executes the analysis. This inversion eliminates the need for users to manually match images with applications, thereby improving ease of operation while reducing the complexity of managing multiple applications.
2Adaptability or versatility
If multiple vendor platforms are used for analysis, then users have access to different analysis capabilities, but analysis results may differ due to variations in vendor platforms and hard-coded parameters
Solution Approach 1:
The patent implements a universal image-centered system that can handle images from multiple vendor platforms (CT, MR, PET, SPECT) through a single unified approach. The system reads image metadata universally and applies appropriate analysis protocols regardless of the vendor platform, thereby maintaining measurement precision and result consistency across different platforms while preserving adaptability to various image types and vendors.
Solution Approach 2:
The patent addresses platform variations by dynamically adjusting parameters based on image metadata rather than using hard-coded parameters specific to each vendor platform. The system extracts relevant parameters from the image data itself and adapts the analysis protocol accordingly, ensuring that analysis results remain consistent and accurate across different vendor platforms without requiring platform-specific hard-coding.
3Reliability
If dedicated analysis tools are developed for different image modalities, then each tool can be optimized for specific image types, but the system requires multiple applications to be installed and managed
Solution Approach 1:
The patent creates a universal image-centered system that can handle multiple image modalities (CT, MR, PET, SPECT) through a single unified platform. Instead of requiring separate dedicated applications for each modality, the system uses image metadata to automatically identify the modality type and apply the appropriate analysis protocol, thereby maintaining the reliability and accuracy optimized for each modality while eliminating the need to install and manage multiple separate applications.
Data Source
AI summary
The present disclosure is directed at a system and method for analyzing clinical trial data over a network. The system and method specifics image protocols, encodes a number of protocols and communicates that information to an acquisition unit that appends the protocols to a digital image. When the image needs to be analyzed to gather clinical trial data, the encoded information is extracted and the correct software application is initialized and used to analyze the image.