Brain Data Subsetting via Dynamic Clinical Prompts
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Solution Overview
Problem
High-dimensional brain data, such as fMRI and diffusion tractography data, is complex and difficult for clinicians to manually inspect and parse for diagnosis or surgical planning, requiring a method to extract clinically relevant subsets efficiently.
Innovation Solution
A system that provides a series of dynamic prompts to users based on clinically relevant questions, determining a subset of brain data relevant to specific conditions or surgeries, and recommends appropriate machine learning models for processing, significantly reducing the amount of data to be analyzed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the entire brain data set is provided to the user for analysis, then complete information is available, but the user must spend excessive time searching and analyzing irrelevant data
Solution Approach 1:
The system extracts only the clinically relevant subset of brain data based on the diagnosed condition, surgical plan, or research question. The data subsetting module filters the large-scale brain data to extract only the portions necessary for the specific clinical task, eliminating irrelevant data while preserving essential information.
Solution Approach 2:
The brain data is segmented into condition-specific subsets using the knowledge graph that organizes data by clinical conditions, anatomical regions, and imaging modalities. This segmentation allows the system to present only the relevant segment of data corresponding to the patient's specific condition and treatment needs.
2Reliability
If comprehensive brain data analysis is performed, then accurate diagnosis is possible, but the complexity of data processing increases significantly
Solution Approach 1:
The knowledge graph serves as an intermediary structure that pre-organizes brain data according to clinical conditions, anatomical regions, and imaging modalities. This intermediary organization enables efficient retrieval and subset extraction without requiring complex real-time processing of the entire data set, thereby maintaining diagnostic accuracy while reducing processing complexity.
3Ease of operation
If the system provides detailed guidance through multiple prompts, then users can easily navigate to relevant data, but the interaction time increases
Solution Approach 1:
The system performs preliminary actions by pre-organizing brain data into a knowledge graph structure and pre-identifying condition-specific data subsets before user interaction. This preliminary organization allows the system to quickly retrieve and present relevant data with minimal prompting, reducing interaction time while maintaining ease of navigation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a subset of brain data of a patient. One of the methods includes obtaining data characterizing a brain of a patient; determining a first prompt for presentation to a user; obtaining a first user input characterizing a first response to the first prompt; determining, using the first response to the first prompt, a second prompt for presentation to the user; obtaining a second user input characterizing a second response to the second prompt, wherein at least one of the first prompt or the second prompt seek a response based on a clinical observation of the patient; and determining a subset of the obtained data using the first response to the first prompt and the second response to the second prompt.