Optical Surface Topography for Rapid Antimicrobial Susceptibility Testing
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Solution Overview
Problem
Current diagnostics for determining antimicrobial susceptibility require long incubation times, leading to inappropriate antibiotic use and the emergence of drug-resistant bacteria due to delayed treatment decisions.
Innovation Solution
A system and method using optical imaging to rapidly determine antimicrobial susceptibility by analyzing surface topography, including metrics such as volumetric and distribution metrics, to identify susceptibility, resistance, and heteroresistance of microorganisms to antimicrobial agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional incubation methods are used to determine antimicrobial susceptibility, then measurement accuracy is maintained, but testing time is excessively long
Solution Approach 1:
The patent replaces traditional mechanical/incubation-based susceptibility testing with optical imaging and machine learning analysis. Optical images of microbial growth are captured and processed through trained machine learning models that can rapidly classify susceptibility phenotypes (susceptible, resistant, heteroresistant) without requiring long incubation periods, thereby reducing testing time while maintaining diagnostic accuracy
Solution Approach 2:
The patent changes the measurement parameters from traditional colony count or zone diameter measurements to optical image features analyzed by machine learning. By transforming the data representation and using automated image analysis, the system achieves rapid susceptibility determination within hours rather than days, resolving the time-accuracy contradiction
2Productivity
If rapid diagnostics are implemented to reduce testing time, then productivity increases, but measurement precision may be compromised
Solution Approach 1:
The patent employs optical imaging systems combined with machine learning algorithms to replace traditional slow diagnostic methods. The optical images capture microbial growth patterns, and trained machine learning models rapidly analyze these patterns to determine susceptibility phenotypes, achieving both high diagnostic speed and maintained accuracy through automated pattern recognition
Solution Approach 2:
The patent uses optical imaging to create visual copies of microbial growth patterns, which are then analyzed by machine learning models. This copying approach allows rapid non-contact analysis of growth morphology without disturbing the sample, enabling fast diagnostics while preserving measurement precision through digital image processing
Data Source
AI summary
In some embodiments, the systems and methods of the disclosure can rapidly and accurately determine the level of susceptibility of a sample to one or more antimicrobial agents using measured topography of that sample. The method may include providing a container including one or more sites having a sample and one or more concentrations of one or more antimicrobial agents. The method may include determining one or more metrics of at least a region of each site using the topographic surface profile for each site. The one or more metrics may include one or more of volumetric, distribution, spatial correlation, among others, or a combination thereof. The method may include determining one or more indices representing a level of susceptibility of the sample to the concentration of the one or more antimicrobial agents provided in each site using the one or more metrics for that site from one or more indices.