Smartphone Microorganism Detection via Image Classifier
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Solution Overview
Problem
Current methods for detecting microorganisms in milk and livestock samples are slow due to the time required for bacterial growth and sample shipping, leading to delays in diagnosis and treatment.
Innovation Solution
A method using a visual spectrum image capture system with a smartphone or tablet, employing a pre-trained image classifier algorithm to identify microorganisms based on growth patterns on a test plate with multiple growth medium regions, allowing for rapid analysis and reduced lead times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional testing methods are used (shipping samples to labs for analysis), then accurate microorganism identification can be achieved, but the testing time and lead time are excessively long
Solution Approach 1:
The patent introduces an intermediary image capture device that photographs the test plate at the farm, serving as a mediator between the physical sample and the remote laboratory analysis. This intermediary captures visual data that can be transmitted electronically, eliminating the need to physically transport the actual sample while preserving diagnostic information.
Solution Approach 2:
The patent creates a visual copy (image) of the test plate with bacterial growth patterns. This image copy contains sufficient information for microorganism identification and can be transmitted digitally to remote laboratories, replacing the need to transport the physical sample while maintaining diagnostic accuracy.
2Measurement precision
If manual expert analysis of test plates is used, then accurate microorganism determination can be achieved, but the productivity and testing capacity are limited
Solution Approach 1:
The patent makes the image capture device and algorithm universally applicable to multiple test plates and various microorganism types. A single automated system can analyze numerous samples across different farms, replacing the need for multiple specialized experts and significantly increasing testing capacity while maintaining accuracy through consistent algorithmic analysis.
Solution Approach 2:
The patent implements an automated image analysis algorithm that performs microorganism identification without requiring manual expert intervention for each sample. The system serves itself by automatically capturing images, processing them through the algorithm, and generating results, thereby eliminating the bottleneck of manual expert analysis and increasing productivity.
3Adaptability or versatility
If multiple test plates with different growth media are used to identify various microorganisms, then the versatility of detection is improved, but the device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical/physical complexity of managing multiple specialized test plates with a digital image analysis system. Instead of requiring different physical media for different organisms, the system uses a standardized test plate captured through a digital camera, with an algorithm that can identify various microorganisms based on visual characteristics, thereby reducing physical complexity while maintaining versatility.
Solution Approach 2:
The patent changes the detection approach from physical/chemical parameters (different growth media compositions) to visual parameters (color, shape, size of colonies in images). This parameter transformation allows a single test plate configuration to detect multiple microorganism types by analyzing visual characteristics through image processing, reducing the need for multiple specialized plates.
Data Source
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AI summary
The present document discloses a method of processing a sample obtained from a livestock animal, comprising applying at least some of said milk to a test surface of a growth medium test plate, waiting for a time sufficient to allow microbial growth to form on said test surface, acquiring a visual spectrum image depicting at least part of the test surface, using an image capture device, and providing a computer-implemented pre-trained image classifier algorithm, said image classifier algorithm being pre-trained to determine a microorganism type based on a visible spectrum image depicting a growth pattern of a known microorganism, and applying said image to the pre-trained image classifier algorithm to determine a microorganism type based on a microorganism growth pattern visible on the image. The document also discloses a method of training an image classifier algorithm, an image capture support for use in acquiring the image, a system comprising the image capture support, a user device and a central processing device, and the use of a pre-trained image classifier algorithm for determining a microorganism type based on a visible spectrum image depicting a microorganism growth pattern on a growth medium containing test plate.