Rapid antibacterial susceptibility testing (RAST) methods which measure change of a bacterial phenotype in response to a given treatment are of significant importance in healthcare, as they can assist care-givers in timely administration of the right treatment. Various RAST techniques have been reported for tracking bacterial phenotypes, such as size, shape, motion, and metabolic activity. However, they still require bulky and expensive instruments (which hinders their application in resource-limited environments) and/or utilize labeling reagents (which can interfere with antibiotics and add to cost). Furthermore, the existing ultra-rapid methods do not address possible adaptation of gradual adaptation of bacteria to antibiotics, which can lead to false interpretation of resistance when using ultra-rapid methods. In this work, we present a RAST approach leveraging machine learning analysis of time-resolved dynamic laser speckle imaging (DLSI) results to accurately predict the minimum inhibitory concentration (MIC) of a model strain of Escherichia coli in 60 minutes, compared to 6 hours using the currently FDA-approved phenotype-based RAST technique. To demonstrate the DLSI performance, we studied the effect of a -lactam ampicillin and an aminoglycoside gentamicin on Escherichia coli strain K-12. DLSI captures change of bacterial motion/division in response to treatment. The machine learning algorithm was trained and validated using the overnight results of gold standard, broth

Rapid antibacterial susceptibility testing (RAST) methods which measure change of a bacterial phenotype in response to a given treatment are of significant importance in healthcare, as they can assist care-givers in timely administration of the right treatment. Various RAST techniques have been reported for tracking bacterial phenotypes, such as size, shape, motion, and metabolic activity. However, they still require bulky and expensive instruments (which hinders their application in resource-limited environments) and/or utilize labeling reagents (which can interfere with antibiotics and add to cost). Furthermore, the existing ultra-rapid methods do not address possible adaptation of gradual adaptation of bacteria to antibiotics, which can lead to false interpretation of resistance when using ultra-rapid methods. In this work, we present a RAST approach leveraging machine learning analysis of time-resolved dynamic laser speckle imaging (DLSI) results to accurately predict the minimum inhibitory concentration (MIC) of a model strain of Escherichia coli in 60 minutes, compared to 6 hours using the currently FDA-approved phenotype-based RAST technique. To demonstrate the DLSI performance, we studied the effect of a -lactam ampicillin and an aminoglycoside gentamicin on Escherichia coli strain K-12. DLSI captures change of bacterial motion/division in response to treatment. The machine learning algorithm was trained and validated using the overnight results of gold standard, broth microdilution method. Empowered by machine learning, DyRAST can predict MIC with high accuracy comparable to gold standard methods through a voting strategy.