Brain Image Analysis for Cognitive Impairment Detection
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Solution Overview
Problem
Current medical imaging techniques face challenges in accurately predicting cognitive impairment due to individual anatomical variability, making it difficult to distinguish between different cognitive disorders such as Alzheimer's disease and mild cognitive impairment using structural brain images.
Innovation Solution
A computer-implemented method that identifies specific image regions in brain images, such as the cerebral cortex, hippocampus, and amygdala, and determines quantitative image metrics to create indicators for predicting or diagnosing cognitive impairment by comparing them with reference data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structural brain imaging techniques are used to predict cognitive function, then spatial resolution and tissue distinction capability are improved, but individual anatomical variability makes accurate prediction difficult
Solution Approach 1:
The patent transforms structural imaging parameters into functional prediction parameters by establishing quantitative relationships between anatomical measurements (volume, surface area, cortical thickness) and cognitive function scores. This allows structural data to predict functional outcomes despite individual variability.
Solution Approach 2:
The patent introduces standardized anatomical models and normalization procedures as intermediaries between individual brain structures and cognitive function predictions. By mapping individual anatomy to standardized models, the system enables reliable cross-subject comparisons and predictions.
2Loss of information
If functional neuroimaging techniques are used to investigate cognitive function, then functional information is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts functional prediction capability from complex functional imaging systems by using structural imaging data combined with machine learning algorithms. This extracts the essential functional prediction function while eliminating the need for complex functional imaging equipment.
Solution Approach 2:
The patent replaces complex functional imaging mechanical systems with a computational approach using machine learning models that process structural imaging data to predict cognitive function, substituting physical measurement complexity with algorithmic processing.
3Adaptability or versatility
If brain images are mapped onto normalized anatomical models to facilitate comparisons, then adaptability for population studies is improved, but mapping accuracy and preservation of individual characteristics deteriorate
Solution Approach 1:
The patent segments the brain into distinct anatomical regions (hippocampus, amygdala, cortex, ventricles) and processes each segment independently with region-specific normalization parameters. This allows simultaneous achievement of cross-subject comparability and preservation of individual regional characteristics.
Data Source
AI summary
A computer implemented method by which digital images of the human brain can be used to diagnose or to predict cognitive impairment, such as Alzheimer's disease and other forms of cognitive impairment such as so-called prodromal Alzheimer's disease. Methods of classifying or stratifying cohorts of human subjects such as for the purpose of clinical trials and/or to assess the impact of therapies are included. In some embodiments the images comprise T1 weighted MRI images.