Brain Region Mapping With Automated Model Selection for Clinical Outcomes
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Solution Overview
Problem
Existing systems for localizing brain areas related to diseases, symptoms, or clinical measurements are time-consuming, expensive, and require disease-specific expertise, making them burdensome for patients and medical professionals.
Innovation Solution
A system that standardizes medical images and uses machine learning models to automatically map brain regions to clinically relevant outcomes, reducing the need for technical or medical expertise and providing an interactive GUI for user input and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to detect early manifestation of Alzheimer's through multiple tests and imaging sessions, then diagnostic accuracy can be achieved, but the process becomes time-consuming and expensive
Solution Approach 1:
The system pre-trains machine learning models using extensive datasets of brain images and clinical outcomes before actual diagnosis is needed. These pre-trained models can rapidly analyze new patient data without requiring multiple imaging sessions, enabling fast accurate diagnosis while reducing time consumption.
Solution Approach 2:
The patent replaces traditional manual analysis methods and multiple physical imaging sessions with automated machine learning models that process brain images digitally. This substitution eliminates the need for repeated imaging procedures while maintaining diagnostic accuracy, thereby reducing time and cost.
2Measurement precision
If traditional brain analysis methods are used, then diagnostic results can be obtained, but they require disease-specific expertise and are burdensome for medical professionals
Solution Approach 1:
The machine learning system performs self-service by automatically analyzing brain images and generating diagnostic insights without requiring manual intervention from medical professionals. The system independently processes images, identifies brain regions, and provides diagnostic recommendations, freeing clinicians from burdensome analysis work while maintaining high diagnostic capability.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between brain imaging data and diagnostic conclusions. This intermediary automatically processes complex image data and translates it into actionable diagnostic information, eliminating the need for medical professionals to directly interpret complex imaging data while preserving diagnostic accuracy.
3Measurement precision
If multiple machine learning models are trained on different brain data, then mapping accuracy for different outcomes can be improved, but model selection complexity increases
Solution Approach 1:
The system incorporates feedback mechanisms that automatically evaluate model performance on validation datasets and select the most appropriate pre-trained model for each new analysis task. This automated feedback loop assesses model accuracy, precision, and relevance to the specific clinical outcome being investigated, eliminating manual model selection complexity while maintaining high mapping accuracy across different brain regions and outcomes.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting automatically a machine learning model that locates region(s) of the brain of a subject that is/are associated with a clinically relevant outcome. One of the methods includes: receiving a brain image dataset of a subject; receiving, from a user, an indication of a patient outcome of interest; selecting, based on the indication of a patient outcome of interest, a model from a plurality of models to produce a selected model; determining brain data of interest for the patient outcome of interest; determining, using the selected model, subject specific brain data of interest based on the brain image dataset of the subject and on the brain data of interest; and taking an action based on the subject specific brain data of interest.