Automated Microscopy Pathogen Detection via Machine Vision
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Solution Overview
Problem
Current methods for detecting parasitic infections in bodily fluids are labor-intensive, require trained personnel, and are not suitable for high-throughput screening, especially in resource-limited settings, leading to challenges in rapidly identifying pathogens like Malaria and Babesiosis, which can be fatal in immunocompromised individuals.
Innovation Solution
An automated apparatus using automated microscopy and machine-vision algorithms for rapid detection of pathogens, including parasites, in bodily samples, which includes a cartridge support frame, optical imaging system, and processor for image processing and classification, capable of identifying pathogens with minimal human involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual microscopy methods are used for detecting parasitic infections, then measurement precision (detection accuracy) is maintained at high levels, but productivity (throughput) is low and labor intensity is high
Solution Approach 1:
The patent replaces manual mechanical microscopy operations with an automated imaging system that uses digital cameras, computer processors, and machine vision algorithms to perform sample analysis, eliminating the need for manual slide preparation and microscopic examination while maintaining detection accuracy
Solution Approach 2:
The system creates digital copies (images) of biological samples and analyzes these copies using computer processing algorithms rather than directly examining physical samples under microscopes, enabling automated high-throughput screening while preserving measurement precision
2Extent of automation
If automated microscopy with machine vision is implemented, then productivity (throughput) increases and labor requirements decrease, but device complexity increases
Solution Approach 1:
The automated microscopy system is designed to perform multiple functions including sample imaging, digital capture, automated analysis, and pathogen identification using a single integrated platform, making the system versatile for various parasitic infections without requiring separate specialized equipment for each function
3Loss of time
If rapid testing methods are developed for parasitic infections, then time for diagnosis is reduced, but measurement precision (detection accuracy) may be compromised
Solution Approach 1:
The system performs continuous automated imaging and analysis of samples without interruption, processing multiple samples sequentially through automated mechanisms that maintain consistent high-precision detection while reducing overall diagnosis time compared to manual methods
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 apparatus enables rapid, accurate detection of pathogens with high sensitivity and specificity, reducing the need for trained personnel and increasing throughput, while maintaining or exceeding the sensitivity of traditional methods, and can identify multiple pathogens in a single test, overcoming issues of rapid antigen mutation.
Implementation Method 1
an optical imaging system having an optical path and comprising: at least one light source, at least one lens, and at least one digital camera
Implementation Method 2
at least one light source, at least one lens, and at least one digital camera; wherein the cartridge support frame is located within or may be moved into the optical path
Data Source
AI summary
Apparatus and methods are described including a digital camera, and a computer processor configured to drive the digital camera to acquire, for each of a plurality of imaging fields of a stained bodily sample, three or more digital images. At least one of the images is a brightfield image and at least two of the images are fluorescent images, each of the fluorescent images being acquired using respective first and second filters, which are different from each other. The computer processor performs image processing on the digital images, by extracting visual classification features from each of the three or more digital images, and identifies one or more entities that are contained within the bodily sample, based upon the image processing. Other applications are also described.