Filterless Fluorescence Imaging for Rapid Cellular Entity Detection
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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 take days to provide accurate results, and autofluorescence-based detection is hindered by weak signals and interference from background light.
Innovation Solution
A device using a single wavelength light source to induce fluorescence, combined with a filter-less image sensor and machine learning models, allows for rapid, accurate detection and classification of cellular entities by analyzing fluorescence patterns without the need for emission filters or filter wheels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional culture methods are used to detect pathogens, then accurate detection can be achieved, but the detection process takes 1-2 days and requires specialized microbiology facilities
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 culture incubation, thereby reducing detection time from 1-2 days to a much shorter period while maintaining detection accuracy through fluorescence signal analysis
Solution Approach 2:
The patent introduces fluorescence markers as intermediaries to enhance pathogen detection. These fluorescent markers bind to or are taken up by pathogens, making them visible under fluorescence microscopy. This intermediary approach allows for rapid detection without the need for extended culture periods, as the fluorescence signal provides immediate visual indication of pathogen presence
2Productivity
If autofluorescence-based detection is used, then rapid detection can be achieved, but the weak signals are hindered by interference from background light
Solution Approach 1:
The patent utilizes fluorescence emission at specific wavelengths that differ from the excitation light wavelength. The fluorescence emission spectrum is shifted to longer wavelengths compared to the excitation light, allowing optical filters to separate the weak fluorescence signal from the intense background light. This wavelength-based color separation enables accurate detection of weak autofluorescence signals despite background interference
Solution Approach 2:
The patent employs optical filters as intermediaries to separate the fluorescence signal from background light. These filters are positioned between the sample and the detector to selectively transmit fluorescence wavelengths while blocking excitation light and other background interference, thereby enhancing signal-to-noise ratio and enabling accurate rapid detection
3Measurement precision
If emission filters and filter wheels are used in fluorescence detection, then background light interference can be reduced, but the device complexity and cost increase
Solution Approach 1:
The patent extracts and utilizes the natural fluorescence emission properties of pathogens themselves rather than relying on complex filter systems. By detecting the inherent fluorescence signals emitted by pathogens at their characteristic wavelengths, the system achieves good signal-to-noise separation without requiring expensive emission filters or filter wheels, thereby reducing device complexity while maintaining detection precision
Solution Approach 2:
The patent employs machine learning algorithms that automatically analyze fluorescence images and distinguish pathogen signals from background interference. This self-service approach uses computational intelligence to perform the function traditionally accomplished by complex optical filtering systems, thereby simplifying the hardware while maintaining or even improving measurement precision through intelligent pattern recognition
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 quick, cost-effective, non-invasive, and in-situ detection and classification of pathogens and tissues, reducing detection time and equipment complexity while maintaining high accuracy.
Implementation Method 1
A device may include a light source to illuminate a target suspected of having a cellular anomaly, such as a pathogen or a cancerous tissue. 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.