Multi-sensor System for Neurodegenerative Disease Diagnosis
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Solution Overview
Problem
Current methods for diagnosing neurodegenerative diseases like Alzheimer's and Parkinson's are invasive, costly, and not highly accurate, while traditional exhaled gas sensors are vulnerable to environmental factors and unable to accurately measure the composition of mixed gases.
Innovation Solution
A multi-sensor system using deep learning technology that generates and measures different reactive signals through interactions with target materials and sensors based on graphene oxide or ssDNA-bound graphene, allowing for the diagnosis of diseases by determining the presence and mixing ratio of biomarker proteins or gases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional MRI and PET tests are used to diagnose neurodegenerative diseases, then diagnostic accuracy is improved, but testing costs increase significantly
Solution Approach 1:
The patent employs disposable, low-cost sensor elements that can detect disease biomarkers without requiring expensive reusable equipment like MRI or PET scanners. These sensors are designed for single-use or limited-use applications, eliminating the need for costly maintenance and calibration while providing accurate diagnostic information at a fraction of the cost of traditional imaging methods.
Solution Approach 2:
The patent replaces complex mechanical and computational imaging systems (MRI and PET) with simple chemical sensing mechanisms. Instead of using magnetic fields, radio waves, and complex image reconstruction algorithms, the invention uses direct chemical interactions between sensor materials and disease biomarkers to produce detectable signals, dramatically simplifying the diagnostic process while maintaining accuracy.
2Ease of operation
If traditional exhaled gas sensors with metal-based structure are used, then gas detection is achieved, but sensor accuracy decreases due to vulnerability to environmental factors like moisture
Solution Approach 1:
The patent uses composite material structures for sensors that combine multiple materials with complementary properties. Specifically, it employs metal-oxide-semiconductor composites and graphene-based composite structures that integrate the gas-sensing capabilities of metals with the environmental stability of semiconductors and carbon materials, creating sensors that maintain high accuracy even in humid conditions.
Solution Approach 2:
The patent employs thin-film sensor structures that are deposited as ultra-thin layers on substrate surfaces. These thin films provide large surface area for gas interaction while maintaining structural integrity and resistance to environmental degradation. The thin-film configuration allows the sensor to detect gas molecules effectively while being protected from moisture and other environmental factors that would degrade bulk metal sensors.
3Loss of information
If existing mixed gas identification technology using deep learning is used, then gas type identification is achieved, but the ability to accurately determine the ratio of detailed mixed gas is insufficient
Solution Approach 1:
The patent divides the gas analysis task into multiple specialized sensor channels, each optimized for detecting specific gas components or concentration ranges. Instead of using a single sensor type, the system segments the detection function across multiple sensor varieties (different metal oxides, graphene-based sensors, and other specialized detectors), allowing each sensor to excel at its specific detection task while collectively providing comprehensive mixed gas analysis with accurate ratio determination.
Solution Approach 2:
The patent creates a multi-functional sensor system where each sensor element can respond to multiple gas types but with different sensitivities. By designing sensors with broad but differentiated response characteristics, the system achieves universal detection capability across various gas compositions while maintaining the precision needed to determine exact gas ratios through pattern recognition and signal analysis.
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 rapid, non-invasive, and accurate diagnosis of neurodegenerative diseases and other conditions by effectively detecting biomarker proteins and gases in exhaled breath, improving diagnostic speed and reducing costs.
Implementation Method 1
a plurality of sensors generate different electric signals for a target material and transmit the different electric signals to a plurality of electrode portions, respectively
Implementation Method 2
interactions with target materials and sensors based on graphene oxide or ssDNA-bound graphene
Data Source
AI summary
An embodiment provides a multi-sensor and an early diagnosis system using the same, wherein the multi-sensor diagnoses a disease by using deep learning technology after obtaining a plurality of different electric signals obtained through reactions with target materials that respectively contact a plurality of sensors equipped in the multi-sensor.