Colony Topography Imaging for Rapid Pathogen Identification
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Solution Overview
Problem
Current methods for bacterial species identification, such as MALDI-TOF mass spectrometry, are time-consuming and costly, limiting their accessibility and efficiency in clinical settings.
Innovation Solution
A novel method using advanced imaging techniques like white light interferometry and coherence scanning interferometry to generate topographical maps of biological specimens, combined with computer-implemented computational algorithms, for rapid and cost-effective pathogen identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MALDI-TOF mass spectrometry is used for bacterial species identification, then identification accuracy is improved, but identification time increases due to required bacterial cultivation
Solution Approach 1:
The patent performs bacterial cultivation and growth in advance, then uses automated image analysis of the grown colonies to identify species characteristics. By preparing the bacterial samples beforehand and using image processing algorithms to analyze colony morphology, the system eliminates the need for time-consuming MALDI-TOF measurement steps while maintaining identification accuracy through preliminary growth and automated analysis.
2Measurement precision
If MALDI-TOF mass spectrometry devices are deployed, then pathogen identification capability is improved, but cost increases significantly
Solution Approach 1:
The patent creates digital copies of bacterial colonies through imaging and uses computational algorithms to analyze these copies instead of requiring physical MALDI-TOF mass spectrometry analysis. By capturing images of bacterial colonies and processing them through image analysis software, the system reproduces the identification function at a fraction of the cost of actual mass spectrometry equipment.
Solution Approach 2:
The patent replaces expensive, durable MALDI-TOF mass spectrometry devices with inexpensive, disposable imaging systems and computational analysis. Instead of investing in costly mass spectrometry hardware, the system uses affordable digital cameras or imaging devices combined with software algorithms to achieve the same identification capability at minimal cost.
3Measurement precision
If conventional pathogen identification methods are used, then diagnostic accuracy is improved, but healthcare resource efficiency decreases
Solution Approach 1:
The patent changes the measurement parameters from time-intensive mass spectrometry analysis to rapid image capture and computational processing. By transforming the identification process from physical/chemical analysis to digital image analysis, the system maintains diagnostic accuracy while dramatically improving throughput and reducing resource consumption per test.
Solution Approach 2:
The patent replaces the mechanical and physical systems of MALDI-TOF mass spectrometry with an optical-digital system based on imaging and computational algorithms. This substitution eliminates the need for complex mass spectrometry hardware and replaces it with lighter, faster, and more resource-efficient image processing technology that achieves the same diagnostic goals.
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 significantly reduces identification time and cost, enabling faster and more accurate diagnosis of infections by identifying pathogens in various biological samples.
Implementation Method 1
imaging at least a portion of the biological specimen to generate data indicative of a topography of the at least a portion of the biological specimen
Implementation Method 2
imaging at least a portion of the biological specimen can comprise performing profilometry
Data Source
AI summary
An exemplary embodiment of the present disclosure provides a method of identifying one or more components in a biological material, the method comprising: providing a biological specimen, the biological specimen comprising one or more components; imaging at least a portion of the biological specimen to generate data indicative of a topography of the at least a portion of the biological specimen; and determining, using a machine learning algorithm, based at least in part on the data indicative of a topography of the at least a portion of the biological specimen, an identity of the one or more components of the biological specimen.


