Machine-Learning Fluorescence Detection of Problematic Cellular Entities
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Solution Overview
Problem
Existing methods for detecting problematic cellular entities, such as pathogens and cancerous tissues, are cumbersome, require specialized facilities, and are time-consuming, often taking 1-2 days, and involve the use of expensive optical filters and filter wheels that increase detection time and cost.
Innovation Solution
A fluorescence-based detection system using a device with a light source, image sensor, and machine learning models like ANN and SVM to analyze fluorescence images without emission filters, enabling rapid, cost-effective, and non-invasive detection and classification of cellular entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional culture methods are used to detect pathogens, then detection accuracy is improved, but detection time increases to 1-2 days
Solution Approach 1:
The patent replaces traditional mechanical culture methods with fluorescence-based optical detection. The system uses fluorescence microscopy to directly visualize and detect pathogens in clinical samples without requiring prolonged incubation periods, reducing detection time from days to hours while maintaining diagnostic accuracy.
Solution Approach 2:
The patent introduces fluorescence markers as intermediaries to enhance pathogen detection. These markers bind to specific pathogen components and emit fluorescence signals that can be detected optically, enabling rapid identification of pathogens without the time-consuming culture process while preserving detection sensitivity.
2Measurement precision
If expensive optical filters and filter wheels are used in fluorescence detection systems, then detection precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent removes the complex filter wheel and multiple optical filters from the fluorescence detection system. Instead, it uses a simplified optical path with a single bandpass filter or even filterless detection methods, extracting only the essential filtering function needed for fluorescence detection while eliminating unnecessary mechanical complexity.
Solution Approach 2:
The patent replaces expensive, mechanically complex filter wheels with simpler, stationary optical filters or computational filtering methods. This substitution uses cheaper, maintenance-free optical elements that achieve the same spectral separation function without moving parts, reducing both cost and device complexity.
3Reliability
If traditional pathogen detection methods are used, then reliable identification is achieved, but the process becomes cumbersome and requires specialized facilities
Solution Approach 1:
The patent enables the detection system to perform multiple functions automatically without requiring specialized facility infrastructure. The fluorescence microscopy system with integrated image analysis software can autonomously detect, visualize, and identify pathogens, eliminating the need for separate culture facilities, biochemical laboratories, and specialized handling procedures.
Solution Approach 2:
The patent creates a universal detection platform that can identify various types of pathogens using a single fluorescence microscopy system. The system handles diverse clinical samples and detects different pathogen types through fluorescence labeling, replacing multiple specialized detection methods and facilities with one multi-functional device.
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
The system provides quick, accurate, and cost-effective detection and classification of pathogens and cancerous tissues, eliminating the need for filter wheels and specialized facilities, and allowing in-situ analysis.
Implementation Method 1
A device may include a light source to illuminate a target suspected of having a problematic cellular entity... The emitted light may be in a wavelength band that causes a marker in the target to fluoresce when illuminated
Data Source
AI summary
Techniques are for detecting presence of a problematic cellular entity in a target. In an example, using an analysis model, a fluorescence-based image is analyzed. The analysis model is trained using a number of reference fluorescence-based images for detecting the presence of problematic cellular entities in targets. Based on the analysis, a problematic cellular entity present in the target is detected. To perform the detection, the analysis model is trained to differentiate between the fluorescence in the fluorescence-based image emerging from the problematic cellular entity and the fluorescence in the fluorescence-based image emerging from regions other than the problematic cellular entity.