Dynamic Laser Speckle Imaging Meets Machine Learning to Enable Rapid Antibacterial Susceptibility Testing (DyRAST)

• Published in: ACS sensors Vol. 5; no. 10; pp. 3140 - 3149
• Main Authors: Zhou, Keren, Zhou, Chen, Sapre, Anjali, Pavlock, Jared Henry, Weaver, Ashley, Muralidharan, Ritvik, Noble, Josh, Chung, Taejung, Kovac, Jasna, Liu, Zhiwen, Ebrahimi, Aida
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 23-10-2020
• Subjects: Anti-Bacterial Agents - pharmacology; Escherichia coli; Lasers; Machine Learning; Microbial Sensitivity Tests; phenotype; laser speckle imaging; antibacterial susceptibility testing; bacteria; machine learning
• ISSN: 2379-3694; 2379-3694
• DOI: 10.1021/acssensors.0c01238

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.