Brain Connectome Visualization for Interpretable ML Diagnosis
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Solution Overview
Problem
Current methods for interpreting outcomes from machine learning models in medical imaging, particularly for brain data, face challenges in handling high-dimensional and complex data sets, making it difficult to understand the relationships between brain parcels and networks in the context of brain pathology.
Innovation Solution
A method that processes brain data using a machine learning model to identify high-impact brain parcels and their respective network affiliations, generating visualizations that explain the predicted outcomes at different hierarchical levels, enabling clinicians to interpret complex connectome interactions and make informed diagnoses or treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to process brain data, then prediction accuracy for medical conditions is improved, but interpretability of the results deteriorates
Solution Approach 1:
The patent segments the complex machine learning prediction process into interpretable components by identifying and visualizing specific brain parcels and their network affiliations that contribute to predictions. This segmentation allows clinicians to understand which specific brain regions and networks drive the model's predictions, maintaining interpretability while using sophisticated ML models.
Solution Approach 2:
The patent introduces an intermediary visualization layer that translates complex machine learning outputs into clinically interpretable formats. This intermediary system maps high-dimensional prediction results to visual representations of brain parcels and networks, bridging the gap between accurate but opaque ML predictions and clinician-needs-for-understandability.
2Loss of information
If detailed brain parcel analysis is performed, then understanding of brain network interactions is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the most relevant brain parcels and network affiliations that contribute to medical condition predictions, rather than analyzing all possible brain regions. This selective extraction reduces computational complexity while maintaining the understanding of critical brain network interactions relevant to the specific medical condition.
Solution Approach 2:
The patent applies local quality by focusing computational resources on specific brain parcels and networks that are locally relevant to the medical condition being predicted. Instead of uniformly analyzing the entire brain, the system identifies and analyzes only those regions with significant contributions to the prediction, reducing overall computational complexity while preserving critical insights.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for visualizing complex connectome interactions relevant to a medical condition. One of the methods includes receiving brain data for a brain of a patient, processing the brain data to determine multiple brain parcels that are predicted to be relevant to a medical condition, determining, for each of multiple brain parcels that are predicted to be relevant to the medical condition, a respective brain network affiliation, and providing, to a user device of a user, data for displaying a visualization that includes one or more respective brain network affiliations determined for each of multiple brain parcels that are predicted to be relevant to the medical condition.