Automated Pathogen Detection Using Microscope Image Feature Extraction
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Solution Overview
Problem
Current methods for detecting pathogens in bodily samples, such as blood, rely on manual microscopic examination which is prone to variability and requires specialized equipment, leading to inaccuracies and accessibility issues, especially in rural settings.
Innovation Solution
A system that acquires microscope images of stained bodily samples using a microscope system and processes them with a computer processor to identify pathogen candidates by extracting informative features, classifying the likelihood of infection, and generating outputs based on these classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual microscopic examination is used for pathogen detection, then specialized equipment and skilled personnel are required, but this leads to reduced accessibility and increased variability in results
Solution Approach 1:
The patent creates a digital copy of the microscopic image and processes it through image analysis algorithms. The system captures images of bodily samples, extracts features automatically, and classifies pathogen presence without requiring manual interpretation, thus making the detection process accessible in settings without skilled microscopists while maintaining reliable results through automated analysis
Solution Approach 2:
The patent replaces the mechanical process of manual microscopic examination with an automated image processing system. Instead of relying on human eyes and manual identification, the system uses computational algorithms to analyze digital images, extract morphological features, and classify pathogens, thereby eliminating the need for specialized personnel while maintaining detection reliability
2Measurement precision
If manual identification of pathogens is performed, then skilled personnel are required, but this causes variability in results depending on the examiner's skill and pathogen levels
Solution Approach 1:
The patent segments the pathogen detection process into distinct automated steps: image acquisition, feature extraction, and classification. The system automatically extracts morphological features such as size, shape, and texture of pathogen candidates, then classifies them based on predefined criteria, eliminating variability introduced by human examiners while maintaining precise detection through consistent algorithmic processing
Solution Approach 2:
The system performs self-service by automatically analyzing its own outputs. The image analysis system processes captured images, extracts features, and generates classifications without requiring external human intervention. This self-service capability ensures consistent, repeatable results that do not vary with examiner skill levels or pathogen concentrations, while the system manages its own complexity through integrated software processing
3Ease of operation
If automated pathogen detection is implemented, then accessibility is improved, but the system complexity increases
Solution Approach 1:
The patent implements a universal image analysis system that can detect multiple types of pathogens by analyzing morphological features. The same hardware and software platform processes different bodily samples and identifies various pathogen types through consistent feature extraction and classification algorithms, making the system accessible for diverse detection needs without requiring separate specialized equipment for each pathogen type
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
This approach automates the detection of pathogens, improving accuracy and accessibility by reducing reliance on skilled personnel and equipment availability, enabling more reliable and efficient diagnosis of infections.
Implementation Method 1
When excited by fluorescent light at an appropriate wavelength, the nucleic acid will fluoresce
Data Source
AI summary
Apparatus and methods are described for analyzing a bodily sample. A microscope system acquires one or more microscope images of the bodily sample. A computer processor identifies elements as being candidates of a given entity, in the one or more images. The computer processor extracts, from the one or more images, at least one candidate-informative feature associated with the candidate, and at least one sample-informative feature that is indicative of contextual information related to the bodily sample. The computer processor processes the candidate-informative feature in combination with the sample-informative feature, and performs an action in response thereto. Other applications are also described.