Brain Parcel Scoring for fMRI Interpretation
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Solution Overview
Problem
Current medical imaging technologies face challenges in interpreting large volumes of fMRI data to diagnose and treat brain-related medical conditions, as existing systems require clinicians to analyze neural activity across numerous brain parcels, making it difficult to identify relevant parcels contributing to specific conditions.
Innovation Solution
A parcel scoring system that processes fMRI data to generate explainability data by computing relative activation scores for only the high-impact parcels relevant to a medical condition, using a machine learning model to predict parcel importance and provide interpretable insights through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If clinicians analyze neural activity across all brain parcels using existing medical imaging systems, then comprehensive brain data is obtained, but data interpretation complexity increases significantly
Solution Approach 1:
The patent segments the brain into discrete parcels and computes relative activation scores for each parcel separately. This segmentation allows clinicians to focus on specific brain regions of interest rather than analyzing all brain data simultaneously, reducing interpretation complexity while maintaining comprehensive coverage through systematic organization of parcel-level analyses
Solution Approach 2:
The patent extracts and highlights only the most relevant parcels that contribute to specific medical conditions, rather than presenting all brain parcel data. By identifying and extracting high-impact parcels based on their contribution to diagnostic accuracy, the system reduces data volume and interpretation complexity while preserving clinically significant information
2Measurement precision
If parcel scoring system focuses only on high-impact parcels, then interpretive value increases, but completeness of analysis may be reduced
Solution Approach 1:
The patent applies local quality by providing detailed, high-precision activation score analysis specifically for high-impact parcels that are most relevant to diagnostic accuracy. Rather than uniformly analyzing all parcels with equal detail, the system concentrates interpretive resources on parcels with higher diagnostic value, improving measurement precision where it matters most while maintaining a systematic approach to ensure no critical information is missed
3Reliability
If machine learning model processes all brain parcels to predict medical conditions, then prediction accuracy improves, but computational resource usage increases
Solution Approach 1:
The patent implements partial action by having the machine learning model process and prioritize only the most informative brain parcels for predicting medical conditions. Rather than exhaustively analyzing all brain parcels with equal computational effort, the system identifies and focuses computational resources on a subset of high-impact parcels that provide the greatest predictive value, thereby maintaining prediction accuracy while reducing overall computational resource consumption
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating explainability data that explains a medical condition in a subject. In one aspect, a method comprises: obtaining data identifying a plurality of brain parcels that are predicted to be relevant to the medical condition; receiving fMRI data for a brain of a subject; processing the fMRI data for the brain of the subject to determine a respective activation score for each of the plurality of brain parcels that are predicted to be relevant to the medical condition; determining, for each of the plurality of brain parcels that are predicted to be relevant to the medical condition, a relative activation score for the brain parcel; and taking an action based on the relative activation scores.