Federated Brain Data Visualization for Protected Medical Analysis
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Solution Overview
Problem
Existing systems face challenges in accessing and analyzing protected medical data, particularly brain data, while adhering to varying privacy policies and protocols, necessitating a solution that allows indirect access and analysis without violating data privacy rules.
Innovation Solution
A framework is implemented that enables federated analysis of brain data by using a first computing platform to communicate with a second platform hosting protected medical data, executing analysis operations remotely while respecting data privacy protocols, and generating visualizations on a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct access to protected medical data is implemented, then analysis operations can be performed efficiently, but privacy policies and protocols are violated
Solution Approach 1:
The patent implements a federated learning framework where a central server acts as an intermediary between multiple data sources. The server coordinates analysis operations without directly accessing protected medical data, instead receiving only aggregated results. This mediator architecture enables efficient cross-institutional analysis while maintaining privacy policy compliance through indirect data access mechanisms.
Solution Approach 2:
The system segments the data access architecture into distinct components: local data sources that retain control over their protected medical data, and a central coordination server that manages analysis workflows. This segmentation allows each entity to operate within their privacy boundaries while still enabling collaborative analysis through structured communication protocols.
2Reliability
If indirect access to protected medical data is implemented through federated analysis, then privacy policies are maintained, but system complexity increases
Solution Approach 1:
The patent implements a universal federated learning framework that handles multiple data sources, various analysis operations, and different privacy policies through a single standardized architecture. The central server provides multi-functional capabilities including workflow coordination, result aggregation, and compliance management, reducing the need for separate systems for each data source.
Solution Approach 2:
The system manages complexity by parameterizing the federated analysis framework with configurable settings for different data sources, analysis types, and privacy requirements. This allows the same core architecture to adapt to varying complexities through parameter adjustment rather than structural modification.
3Adaptability or versatility
If multiple data sources with varying privacy policies are accessed, then comprehensive analysis is enabled, but access coordination becomes difficult
Solution Approach 1:
The patent implements a dynamic access coordination mechanism where the central server adapts its interaction with each data source based on their specific privacy policies and data characteristics. The system dynamically adjusts communication protocols, aggregation methods, and compliance checks to match the requirements of each participating data source, enabling seamless integration of heterogeneous sources.
Data Source
AI summary
The disclosed systems relate to generating brain data visualizations based on federated analysis of brain data. In exemplary embodiments, the brain data visualizations are generated using a system comprising a framework. The framework may include a first computing platform that comprises one or more servers. The framework may also include a first application for communicating via a communication network with a second computing platform that is different from the first computing platform. The second computing platform may comprise a database that stores protected medical data including a plurality of data elements that are directly inaccessible individually or directly inaccessible in aggregate by a user of the first computing platform. In one embodiment, the one or more servers of the first computing platform comprise memory storing instructions that are executable by one or more computer processors of the first computing platform to execute the various processing stages outlined in this disclosure.