NoSQL Imaging Discovery Utility for Cross-Image Clinical Search
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Solution Overview
Problem
Conventional clinical image management systems face challenges in labeling, mining, and associating derived artifacts with images, making it difficult to navigate and search across images regardless of patient association, and there is a need for more robust and scalable solutions for image management.
Innovation Solution
Implementing an imaging discovery utility using a non-relational (NoSQL) data management system that couples with a relational (SQL) database, enabling dynamic tagging, searching across all images, and linking images to artifacts, with a structured database containing minimum required data fields and an unstructured database enhancing data fields based on user needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional commercial clinical image management systems are used, then secure patient-at-a-time access is maintained, but navigation and search across images becomes difficult and labeling/mining/associating derived artifacts with images becomes impossible
Solution Approach 1:
The system separates the database into two distinct components: a structured SQL database for patient-centric storage and an unstructured NoSQL database for image-centric metadata and artifact storage. This segmentation allows independent optimization of each database type for its specific purpose, enabling efficient cross-image search while maintaining patient access security.
Solution Approach 2:
The unstructured NoSQL database acts as an intermediary layer between the structured SQL database and the image artifacts. It stores metadata tags and derived artifacts separately, allowing users to search and navigate images through tags without directly querying the patient records, thus facilitating ease of operation while maintaining system security and integrity.
2Adaptability or versatility
If metadata is stored in a separate repository with no direct link to the image, then data security is maintained, but labeling and mining of images becomes difficult
Solution Approach 1:
The unstructured NoSQL database serves as an intermediary that stores metadata tags and derived artifacts with direct associations to images through unique identifiers. This intermediary layer maintains the security benefits of separate storage while enabling versatile labeling, mining, and association operations by providing direct links between metadata and images in the NoSQL repository.
Solution Approach 2:
The system adds a new dimension to data storage by introducing the unstructured NoSQL database layer. This additional dimension allows metadata and artifacts to be stored with direct image associations without compromising the original patient-centric security model, enabling advanced search and mining capabilities across multiple dimensions of data access.
3Ease of operation
If artifacts are attached as an embedded list, then data organization is simplified, but navigation through attachments becomes problematic
Solution Approach 1:
The system segments the artifact storage from the main image data by storing derived artifacts in the unstructured NoSQL database separately from the embedded lists in the SQL database. This segmentation allows artifacts to be organized and navigated independently through tag-based metadata, improving ease of operation while the underlying segmented structure handles the complexity of data organization.
4Adaptability or versatility
If a structured database with minimum required data fields is used, then data consistency is maintained, but flexibility for user-specific data enhancement is limited
Solution Approach 1:
The dual-database architecture segments data storage into structured SQL fields for consistent required data and unstructured NoSQL fields for flexible user-specific enhancements. This segmentation allows the system to maintain data consistency through the structured portion while providing adaptability through the unstructured portion that can be customized for different user needs without affecting the core data integrity.
Solution Approach 2:
The system applies local quality by allowing different parts of the data structure to have different characteristics: the SQL database maintains strict consistency and structure for required fields, while the NoSQL database provides flexible, customizable fields for specific user needs. Each database type is optimized for its specific quality requirements, achieving both consistency and adaptability simultaneously.
Data Source
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AI summary
Novel tools and techniques are provided for implementing an imaging discovery utility for augmenting clinical image management. In some embodiments, in response to receiving a request for a first medical image file(s) from a requesting device, a non-relational ("NoSQL") data management system ("DMS") may access a NoSQL database containing, inter alia, a plurality of medical image files that are mirrored copies of a plurality of medical image files stored in a relational ("SQL") database, the medical image files each being organized in an image-centric hierarchy with image data being at a top level and patient information associated with the image data being at a lower level. Based on a successful search of the NoSQL database based on search terms in the request, the NoSQL DMS may identify a corresponding second medical image(s) in the SQL database, and may retrieve and send (to the requesting device) the identified second medical image(s).