LLM Brain Data Anomaly Detection
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Solution Overview
Problem
Clinicians face challenges in manually inspecting and parsing complex, high-dimensional brain functional connectivity data to diagnose brain diseases or mental disorders, especially when planning surgeries.
Innovation Solution
A method involving natural language processing using large language models to identify and display brain regions with abnormal activity levels, based on user-input descriptions of mental states or behaviors, and analyzing MRI data to determine anomalous networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinicians manually inspect and parse brain functional connectivity data, then they can diagnose brain diseases or mental disorders, but the process is time-consuming and difficult due to the complexity and high-dimensionality of the data
Solution Approach 1:
The patent introduces an intermediary system comprising a processor and memory that automatically processes brain functional connectivity data. This intermediary translates complex high-dimensional brain data into clinically actionable insights, bridging the gap between raw data and clinical diagnosis without requiring clinicians to manually parse the complex data structures.
Solution Approach 2:
The patent replaces the manual mechanical process of clinician data inspection with an automated computational system. The processor executes algorithms that automatically analyze brain functional connectivity data, substituting the manual cognitive and physical work of clinicians with automated computational analysis, thereby eliminating time loss while maintaining diagnostic capability.
2Reliability
If clinicians manually analyze complex brain data, then they can identify relevant anomalies, but the complexity and high-dimensionality of the data make the process difficult and time-consuming
Solution Approach 1:
The patent segments the complex brain functional connectivity data into manageable components that can be automatically processed. The system divides the high-dimensional data into distinct analytical modules, each handling specific aspects of the data, thereby reducing the perceived complexity while maintaining comprehensive analysis capability and reliable anomaly detection.
Solution Approach 2:
The patent transforms the complex high-dimensional brain data into simplified parameters and metrics that are easier to process and interpret. By changing the parameter representation from raw high-dimensional connectivity matrices to derived clinical metrics, the system reduces processing complexity while preserving the reliability needed for accurate anomaly detection.
3Loss of information
If manual inspection of brain data is performed, then clinicians can parse the information, but the high-dimensionality and complexity reduce efficiency and increase time requirements
Solution Approach 1:
The patent implements a self-service system where the computational algorithms automatically extract and process information from brain functional connectivity data without requiring manual clinician intervention. The system serves itself by automatically performing data parsing, anomaly detection, and result generation, thereby maintaining complete information extraction while dramatically improving processing efficiency and productivity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a mental state of a patient based on a natural language input and determining whether a relevant subset of brain data is anomalous. One of the methods includes receiving a natural language input describing at least one aspect of a mental state or of a behavior of an individual; generating a prompt based on at least in part the natural language input; submitting the prompt to a large language model; receiving at least one functional network that influences the at least one aspect of a mental state or of a behavior; for each network of the at least one functional network, analyzing MRI data for the individual to determine whether the network is anomalous; displaying to a user each network and whether it is anomalous; and taking an action in response to the displaying.