Dynamic Laser Speckle Imaging Meets Machine Learning to Enable Rapid Antibacterial Susceptibility Testing (DyRAST)
| Autor: | Ritvik Muralidharan, Zhiwen Liu, Jasna Kovac, Chen Zhou, Joshua Noble, Keren Zhou, Jared Henry Pavlock, Ashley Weaver, Anjali Sapre, Aida Ebrahimi, Taejung Chung |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.drug_class
Computer science Antibiotics Cephalosporin Bioengineering Microbial Sensitivity Tests 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Machine Learning Minimum inhibitory concentration Ampicillin Escherichia coli medicine Instrumentation Fluid Flow and Transfer Processes business.industry Lasers Process Chemistry and Technology 010401 analytical chemistry Aminoglycoside Laser Speckle Imaging Gold standard (test) 021001 nanoscience & nanotechnology Anti-Bacterial Agents 0104 chemical sciences Gentamicin Artificial intelligence 0210 nano-technology business computer medicine.drug |
| Zdroj: | ACS Sensors. 5:3140-3149 |
| ISSN: | 2379-3694 |
| DOI: | 10.1021/acssensors.0c01238 |
| Popis: | Rapid antibacterial susceptibility testing (RAST) methods are of significant importance in healthcare, as they can assist caregivers in timely administration of the correct treatments. Various RAST techniques have been reported for tracking bacterial phenotypes, including size, shape, motion, and redox state. However, they still require bulky and expensive instruments-which hinder their application in resource-limited environments-and/or utilize labeling reagents which can interfere with antibiotics and add to the total cost. Furthermore, the existing RAST methods do not address the potential gradual adaptation of bacteria to antibiotics, which can lead to a false diagnosis. In this work, we present a RAST approach by leveraging machine learning to analyze time-resolved dynamic laser speckle imaging (DLSI) results. DLSI captures the change in bacterial motion in response to antibiotic treatments. Our method accurately predicts the minimum inhibitory concentration (MIC) of ampicillin and gentamicin for a model strain of Escherichia coli (E. coli K-12) in 60 min, compared to 6 h using the currently FDA-approved phenotype-based RAST technique. In addition to ampicillin (a β-lactam) and gentamicin (an aminoglycoside), we studied the effect of ceftriaxone (a third-generation cephalosporin) on E. coli K-12. The machine learning algorithm was trained and validated using the overnight results of a gold standard antibacterial susceptibility testing method enabling prediction of MIC with a similarly high accuracy yet substantially faster. |
| Databáze: | OpenAIRE |
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