Anomalous Brain Data Identification System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Highly complex and large-scale brain data from patients, such as correlation matrices from fMRI data, are difficult for clinicians to manually inspect and analyze, making it time-consuming to identify clinically relevant information for diagnosis or surgery planning.
Innovation Solution
A system that processes brain data to identify anomalous pairs of parcellations by comparing them to a normal range defined by data from multiple other patients, using a machine learning model to generate anomaly correlation data and display only clinically relevant findings to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinicians manually inspect and analyze brain data correlation matrices, then they can identify clinically relevant information, but the process becomes extremely time-consuming due to the large size and complexity of the data
Solution Approach 1:
The patent introduces an intermediary system that includes a trained machine learning model which acts as a mediator between the raw brain data and the clinician. This system automatically identifies anomalous correlations in brain data and presents them to clinicians, thereby reducing the time required for analysis while maintaining detection accuracy. The intermediary processing system filters and prioritizes data, allowing clinicians to focus on the most relevant findings.
2Loss of information
If the system displays all brain data correlation matrices to users, then complete information is available, but users are overwhelmed by the large volume of clinically irrelevant data
Solution Approach 1:
The patent applies the extraction principle by automatically identifying and extracting only the anomalous or clinically relevant correlations from the complete brain data set. The system compares individual patient data against a trained model containing normative data, extracts deviations from normal patterns, and presents only these anomalous findings to the clinician. This maintains information completeness for analysis purposes while dramatically improving ease of operation by filtering out normal, non-actionable data.
3Extent of automation
If a machine learning model is trained on data from multiple patients to define normal ranges, then the system can automatically identify anomalies, but the initial setup and training process becomes more complex
Solution Approach 1:
The patent applies preliminary action by performing the complex training process in advance, before actual clinical use. The machine learning model is trained on aggregated brain data from multiple patients to establish normative correlation patterns and define normal ranges. This preliminary training phase, while complex, is executed once during system setup, and the resulting model then enables automatic anomaly identification without requiring repeated complex processing during clinical workflows. The upfront investment in training creates long-term automation benefits.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining anomalous brain data. One of the methods includes obtaining brain data characterizing brain activity of a patient; for each of a plurality of pairs of parcellations comprising a first parcellation and a second parcellation, processing the brain data to generate a correlation between the brain activity of the first and second parcellations; obtaining second connectivity data that characterizes, for each of the plurality of pairs of parcellations, a normal range of correlations between the brain activity of the first and second parcellations; identifying one or more of the plurality of pairs of parcellations for which the correlation between brain activity of the first and second parcellations is outside of the corresponding normal range of correlations; and providing data characterizing the one or more identified pairs of parcellations for display to a user on a graphical interface.