Microorganism Classification via Speckle Pattern Machine Learning
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Solution Overview
Problem
Current diagnostic methods for pathogenic microorganisms are facing challenges due to the misuse of antibiotics, increased use of immunosuppressants, and the rise of antibiotic-resistant bacteria, making it difficult to accurately and rapidly diagnose infectious diseases.
Innovation Solution
An apparatus and method that utilize machine-learning to classify the type and concentration of microorganisms in a sample by analyzing speckle information generated through multiple scattering of waves incident on the sample, using a receiving unit to capture images, a detecting unit to extract features, a learning unit to establish classification criteria, and a determining unit to classify the microorganisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional diagnostic methods are used, then diagnostic accuracy may be maintained, but diagnostic speed and productivity decrease due to complex procedures and chemical methods
Solution Approach 1:
The patent replaces traditional mechanical and chemical diagnostic procedures with an optical system that uses wave scattering and machine learning algorithms. The apparatus captures optical images of microorganism samples and uses automated image processing to identify microorganisms, eliminating the need for complex chemical reagents and manual laboratory procedures.
Solution Approach 2:
The patent changes the diagnostic approach from chemical analysis to optical parameter analysis. By measuring scattering parameters of waves interacting with microorganisms and analyzing these optical parameters through machine learning, the system achieves rapid identification without chemical methods.
2Measurement precision
If traditional chemical methods are used for microorganism identification, then accuracy may be sufficient, but time consumption and loss of time increase
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models with extensive microorganism image data before actual diagnosis. The system prepares classification criteria and algorithms in advance, so that during actual use, only rapid image capture and automated processing are needed, eliminating time-consuming manual analysis.
Solution Approach 2:
The patent creates optical copies (images) of microorganisms through wave scattering patterns and uses these copies for analysis instead of handling the actual biological samples through chemical methods. This allows rapid replication and analysis of multiple samples simultaneously.
3Reliability
If conventional diagnostic approaches are used, then established reliability may be maintained, but adaptability to new pathogens and resistant bacteria decreases
Solution Approach 1:
The patent implements a dynamic diagnostic system where the machine learning models can be continuously retrained and updated with new data. The system adapts to new pathogens and resistant bacteria by incorporating new image data into the training set, allowing the classification criteria to evolve with emerging threats.
Solution Approach 2:
The patent creates a universal diagnostic platform that can identify various types of microorganisms using the same fundamental optical imaging approach. The machine learning framework is designed to handle different classes of pathogens, making the system versatile against diverse threats including antibiotic-resistant bacteria.
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 allows for quick and accurate identification of microorganisms without the need for chemical methods, enhancing the ability to rapidly treat infectious diseases and addressing the challenges posed by antibiotic resistance and other factors.
Implementation Method 1
each of the plurality of images includes speckle information generated by multiple scattering by the microorganisms due to waves incident on the sample
Data Source
AI summary
According to an embodiment of the present disclosure, provided is an apparatus for providing microorganism information, including: a receiving unit configured to receive a plurality of images obtained by photographing in time series an outgoing wave emitted from a sample; a detecting unit configured to extract a feature of a change over time from the plurality of images obtained by photographing in time series; a learning unit configured to machine-learn classification criteria based on the extracted feature; and a determining unit configured to classify the type or concentration of a microorganism included in the sample based on the classification criteria, wherein each of the plurality of images includes speckle information generated by multiple scattering by the microorganism due to waves incident on the sample.