Disease State Classifier for Co-morbid Brain Condition Differentiation
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Solution Overview
Problem
Current methods fail to accurately differentiate and quantify the contributions of co-morbid neurodegenerative diseases in patients, leading to distorted diagnostic results and ineffective treatment plans, as they combine the effects of multiple diseases in brain imaging data.
Innovation Solution
A machine learning approach with a classifier is used to dissociate and quantify the expression of different disease states in imaging data, allowing for the separate tracking of disease progression and identification of specific conditions, such as distinguishing Down Syndrome from Alzheimer's Disease.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging analysis methods are used to assess patients with multiple disease states, then the overall brain condition can be evaluated, but the individual contributions of each co-morbid disease cannot be differentiated
Solution Approach 1:
The patent segments the mixed imaging data into distinct disease-specific components by training separate classifiers for each disease state (e.g., Alzheimer's Disease, Down Syndrome, Parkinson's Disease). Each classifier is trained on imaging data from patients with that specific disease, enabling the system to differentiate and quantify the individual contribution of each co-morbid disease to the overall brain condition.
2Adaptability or versatility
If multiple disease signatures are combined in imaging analysis, then comprehensive patient assessment is achieved, but accurate diagnosis of individual diseases becomes distorted
Solution Approach 1:
The patent introduces disease-specific classifiers as intermediary components that act as mediators between the mixed imaging data and the final diagnosis. Each classifier serves as an intermediary that isolates and processes the signature of its corresponding disease, preventing the mixing of disease signatures and maintaining diagnostic accuracy for each individual disease while still enabling comprehensive multi-disease assessment.
3Ease of operation
If conventional diagnostic approaches are used for patients with co-morbidities, then treatment plans can be developed, but the effectiveness is reduced due to inability to track individual disease progression
Solution Approach 1:
The patent implements a feedback mechanism where the trained classifiers continuously assess new imaging data and provide quantitative measurements of individual disease progression. This feedback loop enables clinicians to track the progression of each co-morbid disease separately over time, allowing for adjusted and optimized treatment plans that address each disease's specific progression rate and response to treatment, thereby improving treatment effectiveness.
Data Source
AI summary
System and methods for identifying a brain condition of a patient subject to a plurality of disease states are provided, in some aspects, the method includes receiving imaging data associated with a patient's brain acquired using an imaging system, and constructing a classifier having signatures corresponding to a plurality of disease states. The method also includes applying the classifier to the imaging data to determine a degree to which the patient expresses at least one of the plurality of disease states, and determining a brain condition of the patient using the determined degree. The method further includes generating a report indicative of the brain condition of the patient.