Plasma FTIR Spectroscopy Disease Screening Model
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Solution Overview
Problem
Current medical screening technologies for complex diseases like Alzheimer's and neurodegenerative diseases are invasive, costly, time-consuming, and not suitable for large-scale screening due to high subjectivity and environmental concerns.
Innovation Solution
A disease screening model building apparatus using FTIR-ATR spectroscopy and machine learning algorithms to acquire and process plasma samples, determining spectral digital biomarkers and building a multi-model fused screening system for rapid, sensitive, and specific disease detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional screening methods (scale assessments, cerebrospinal fluid testing, imaging examinations) are used, then diagnostic accuracy can be maintained, but the screening process becomes invasive, costly, time-consuming, and unsuitable for large-scale screening
Solution Approach 1:
The patent extracts and analyzes specific biomolecular components (proteins, lipids, carbohydrates, nucleic acids) from plasma samples using FTIR spectroscopy. By focusing on the spectral characteristics of these specific components rather than performing comprehensive invasive tests, the system achieves reliable disease detection while reducing invasiveness and operational complexity
Solution Approach 2:
The patent replaces mechanical/invasive testing methods (needle punctures for cerebrospinal fluid, physical contact for scale assessments) with optical detection (FTIR spectroscopy). This substitution eliminates the need for invasive procedures while maintaining diagnostic capability through spectral analysis of plasma biomarkers
2Measurement precision
If comprehensive imaging examinations (fMRI, PET, SPECT) are performed, then disease detection accuracy improves, but testing cost increases significantly making routine screening impossible
Solution Approach 1:
The patent uses disposable plasma samples (small volume, non-invasive collection) instead of expensive, resource-intensive imaging procedures. The plasma membrane preparation and FTIR analysis provide a low-cost alternative that maintains detection accuracy through biomolecular spectral fingerprinting rather than expensive imaging hardware
Solution Approach 2:
The patent extracts diagnostically relevant spectral information from plasma samples, focusing on specific wavenumber regions corresponding to biomolecular vibrations. This extraction of key diagnostic features from simple plasma samples replaces the need for expensive comprehensive imaging while maintaining detection precision
3Reliability
If scale assessments with trained testers are conducted, then diagnostic capability is maintained, but time efficiency decreases and human resource requirements increase
Solution Approach 1:
The patent enables the plasma samples to 'self-reveal' diagnostic information through their inherent spectral properties. The FTIR spectroscopy automatically captures biomolecular vibrations without requiring trained testers to interpret subjective scale assessments, eliminating human resource bottlenecks and significantly improving screening throughput
Solution Approach 2:
The patent replaces the mechanical process of human testers conducting scale assessments with automated optical detection and machine learning analysis. The system objectively quantifies spectral features and automatically classifies disease states, eliminating variability and time constraints of human evaluation
4Reliability
If traditional biochemical tests and imaging markers are used, then diagnostic reliability is maintained, but environmental pollution increases and operational complexity rises
Solution Approach 1:
The patent replaces wet chemistry biochemical tests (which generate chemical waste) with dry optical detection using FTIR spectroscopy. The technique requires no reagents, produces no chemical pollution, and eliminates complex sample preparation procedures, achieving clean, sustainable disease screening while maintaining diagnostic reliability
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 high-throughput, low-invasive, cost-effective, and environmentally friendly complex disease screening with high sensitivity and specificity, suitable for large-scale screening of diseases like Alzheimer's and metabolic disorders.
Implementation Method 1
measure FTIR-ATR spectrum data of plasma membranes of the plasma samples
Data Source
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AI summary
The present application relates to a disease screening model building apparatus, a disease screening apparatus, a device, and a medium. The disease screening model building apparatus comprises: a sample spectrum acquisition and data processing module; a plasma spectral digital biomarker determination module; a disease screening model building module, configured to build a disease screening model according to the training set and the validation set of the plasma samples and the spectral digital disease biomarker set by using a random forest of machine learning and a convolutional neural network of deep learning, and build a multi-model fused screening system; and a disease screening model testing module, configured to test the disease screening model and the fused screening system using the sample data in the test set to obtain a disease fusion screening model based on plasma membrane spectra. The present application can achieve high-throughput complex disease screening that is low-invasive, easy to operate, low-price, reliable in results, and environmentally friendly.