Brain Image Radiomics for Early Neurodegenerative Decline Prediction
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Solution Overview
Problem
Current methods for predicting neurodegenerative decline, such as cognitive impairment and Parkinson's disease, lack the ability to detect early signs of brain atrophy and structural changes before symptoms become clinically apparent, necessitating a more sensitive and efficient diagnostic approach.
Innovation Solution
A computer-implemented method using radiomics and machine learning to analyze medical images, combining texture features from brain structures like the hippocampus and other regions, correlated with clinical and biological data to predict neurodegenerative decline by constructing a predictive model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods (clinical interviews, neuropsychological tests, structural MRI) are used, then diagnosis can be established, but by the time diagnosis is made the disease process is already advanced and irreversible
Solution Approach 1:
The patent performs preliminary detection of neurodegenerative disease by measuring metabolite levels in CSF before traditional diagnostic methods can detect disease. This early detection of metabolic changes allows intervention before irreversible damage occurs, resolving the contradiction by detecting disease at a precursor stage rather than waiting for structural changes.
Solution Approach 2:
The patent replaces mechanical/structural assessment methods (structural MRI, physical examinations) with metabolic measurement methods (LCR-MS metabolite analysis). This substitution detects functional metabolic changes that occur before structural damage, enabling earlier diagnosis without waiting for visible structural changes.
2Reliability
If traditional imaging and clinical tests are used, then diagnosis can be confirmed, but the disease process is already advanced and treatment options are limited
Solution Approach 1:
By detecting metabolic changes before irreversible damage occurs, the patent enables treatment during the pre-symptomatic or early symptomatic stage when disease-modifying therapies are most effective. This preliminary detection maintains high diagnostic reliability while expanding treatment accessibility to patients who would otherwise be ineligible.
Solution Approach 2:
The patent uses CSF metabolite levels as an intermediary biomarker that bridges the gap between early pathological changes and clinical symptoms. This intermediary measurement provides reliable disease detection while enabling earlier intervention, making treatment accessible before the disease reaches advanced stages.
3Measurement precision
If comprehensive neuropsychological testing and multiple imaging modalities are used, then diagnostic accuracy can be improved, but the complexity and cost of the diagnostic process increases
Solution Approach 1:
The patent extracts the key diagnostic information from complex multimodal assessments by focusing specifically on CSF metabolite levels. This extraction of the most informative biomarker simplifies the diagnostic process while maintaining high accuracy, eliminating the need for comprehensive neuropsychological testing and multiple imaging modalities.
Solution Approach 2:
The patent uses LCR-MS technology to create a simplified copy or surrogate measure of disease status through metabolite profiling. This metabolic fingerprint serves as a simpler alternative to complex clinical assessments, providing equivalent diagnostic accuracy with reduced procedural complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables early prediction of neurodegenerative decline, providing therapeutic guidance and improving treatment efficacy by identifying subtle brain changes before clinical detection, with accuracy enhanced by using texture features and machine learning algorithms.
Implementation Method 1
liquid chromatograph-mass spectrometer (LCR-MS) as described herein
Data Source
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AI summary
The present invention relates to a method for predicting neurodegenerative decline and/or its severity for a patient, especially of cognitive impairment (CI). Strokes and Parkinson's disease are frequently associated with occurrence of long-term cognitive impairment or dementia with still incompletely resolved mechanisms. The discovery of diagnostic and predictive biomarkers thus remains a major challenge. The method of the invention uses radiomics corresponding to texture features extracted from a plurality of previously-acquired medical brain images and correlated with previously-acquired clinical and/or biological data. A classifier is trained beforehand for learning these radiomics, and then operated on radiomics computed from at least one brain image of a patient to generate a score representative of its risks of neurodegenerative decline. By applying this method on a cohort of 160 MCI and non-MCI patients, the inventors show that MCI patients could be early predicted with a mean accuracy of 88%. In the same way, the method was able to discriminate very early stages of cognitive decline in a Parkinson's disease population of 100 patients.