An Automated and Cost-Effective System for Early ...
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Abstract and Introduction Early-Detection AST System Results Results (cont.) Acknowledgements An Automated and Cost-Effective System for Early Antimicrobial Susceptibility Testing Using Optical Fibers and Deep Learning 1 Department of Electrical and Computer Engineering; 2 Department of Pathology and Laboratory Medicine; 3 Department of Computer Science; 4 Department of Bioengineering; 5 California NanoSystems Institute (CNSI); 6 Jonsson Comprehensive Cancer Center; 7 Department of Surgery, David Geffen School of Medicine, UCLA Calvin Brown 1 , Derek Tseng 1 , Paige M. K. Larkin 2 , Susan Realegeno 2 , Leanne Mortimer 2 , Arjun Subramonian 3 , Dino Di Carlo 4,5,6 , Omai Garner 2 , Aydogan Ozcan 1,4,5,7 Visit innovate.ee.ucla.edu and org.ee.ucla.edu/hhmi for more info We thank the Howard Hughes Undergraduate Research Program, NSF PATHS-UP, and Vehbi Koç Foundation for their funding and support. Antimicrobial susceptibility testing (AST) is a process to determine the concentration of antibiotics to which a microbe is sensitive. Manual AST is time-consuming and tedious. In addition, technological challenges and high costs have led to AST not being performed routinely, which, in turn, is responsible for high mortality rates in many parts of the world, as well as the global spread of multi-drug resistant bacteria. In recent years, efforts have been made to automate AST. However, these systems are neither cost nor space-efficient. Our system enables automated AST that primarily uses off-the-shelf components and saves hours compared to manual AST. It consists of a Raspberry Pi computer, 3D-printed components, and plastic optical fibers, placed directly in an incubator. The fibers image each well in a susceptibility plate periodically during incubation. Light intensities from the fibers are fed into a fully-connected neural network to predict well turbidity at any given time during incubation. Our system demonstrates the potential for automated, cost-effective AST and antimicrobial resistance monitoring. Antimicrobial Susceptibility Testing (AST) 1. Mix patient’s bacteria with many antimicrobials/concentrations in 96-well plate 2. Incubate for 18-24 hrs. and check which drugs/concentrations stopped growth 3. Measure minimum inhibitory concentration (MIC) for each drug to quantify bacteria’s susceptibility Figure 1 Antimicrobial susceptibility testing not being performed routinely is largely responsible for high mortality rates in many parts of the world. Early-Detection AST System Determines turbidity (growth in well), MIC, and susceptibility (does microbe grow at drug concentration cutoff?). Autonomously monitors bacteria during incubation. Prototype costs under $500. Utilizes 3D- printed housing and off-the- shelf components. Image Processing and Deep Learning Process: 1. Cameras capture images every 5 minutes 2. Fiber intensities extracted from raw images 3. Neural network uses fiber intensities over time to determine turbidity One network for all wells/drugs/concentra tions. Important for real-world use, e.g. drugs, and conditions. Training only needs to be performed once before model can be applied to real-world bacterial growth. Dropout and batch normalization. Conclusion Our device performs early, autonomous AST in a cost-effective manner. Captures images during incubation and aligns with clinical workflow. $500 for all components/electronics vs. $100,000+ for commercial AST readers. Device surpasses FDA criteria (EA, CA, and errors) for automated AST. In the future, will determine optimal number of fibers, test on different bacteria (Gram negative), investigate temperature gradients within incubator Figure 2 How antibiotic resistance happens in a patient’s body. Causes of Antimicrobial Resistance • Natural processes • Overprescription/overuse of antibiotics • Livestock industry • Lack of new drugs Readout Problems 1. Manual reading by trained diagnostician. Inefficient use of diagnostician’s time. Potentially dangerous to patient because requires up to 24 incubation for visible growth. Meanwhile, patient placed on harmful broad-spectrum antibiotics. 2. Automatic reading by commercial AST machines. Expensive ($100,000+). Space-inefficient. Figure 5 Image processing Figure 6. Network turbidity predictions. Essential Agreement Categorical Agreement Major Errors Very Major Errors Avg time to meet (h) 6.13 6.98 4.02 9.39 FDA Criteria > 90% > 90% < 3% < 3% Figure 7 System correctly detects turbidity in blindly-tested clinical Staphylococcus aureus plates after an average of just 5.72 h (compared to 18 h gold standard). Turbidity was correctly identified in 95.03% of cases. cross-validation data. Figure 4 Antimicrobial susceptibility testing system Figure 8 Neural network shows improved performance compared to logistic regression and threshold-based methods. Figure 3 Typical plate, manual reading, and automated reading
Transcript of An Automated and Cost-Effective System for Early ...
Abstract and Introduction Early-Detection AST System
Results
Results (cont.)
Acknowledgements
An Automated and Cost-Effective System for Early Antimicrobial Susceptibility Testing Using Optical Fibers and Deep Learning
1Department of Electrical and Computer Engineering; 2Department of Pathology and Laboratory Medicine; 3Department of Computer Science; 4Department of Bioengineering; 5California NanoSystems Institute (CNSI); 6Jonsson Comprehensive Cancer Center; 7Department of Surgery, David Geffen School of Medicine, UCLA
Calvin Brown1, Derek Tseng1, Paige M. K. Larkin2, Susan Realegeno2, Leanne Mortimer2, Arjun Subramonian3, Dino Di Carlo4,5,6, Omai Garner2, Aydogan Ozcan1,4,5,7
Visit innovate.ee.ucla.eduand org.ee.ucla.edu/hhmi for more info
We thank the Howard Hughes Undergraduate Research Program, NSF PATHS-UP, and Vehbi Koç Foundation for their funding and support.
Antimicrobial susceptibility testing (AST) is a process to determine the concentration ofantibiotics to which a microbe is sensitive. Manual AST is time-consuming and tedious. Inaddition, technological challenges and high costs have led to AST not being performed routinely,which, in turn, is responsible for high mortality rates in many parts of the world, as well as theglobal spread of multi-drug resistant bacteria. In recent years, efforts have been made toautomate AST. However, these systems are neither cost nor space-efficient. Our system enablesautomated AST that primarily uses off-the-shelf components and saves hours compared tomanual AST. It consists of a Raspberry Pi computer, 3D-printed components, and plastic opticalfibers, placed directly in an incubator. The fibers image each well in a susceptibility plateperiodically during incubation. Light intensities from the fibers are fed into a fully-connectedneural network to predict well turbidity at any given time during incubation. Our systemdemonstrates the potential for automated, cost-effective AST and antimicrobial resistancemonitoring.
Antimicrobial Susceptibility Testing (AST)1. Mix patient’s bacteria with many
antimicrobials/concentrations in 96-well plate2. Incubate for 18-24 hrs. and check which
drugs/concentrations stopped growth3. Measure minimum inhibitory concentration
(MIC) for each drug to quantify bacteria’s susceptibility
Figure 1 Antimicrobial susceptibility
testing not being performed routinely is
largely responsible for high mortality rates in many
parts of the world.
Early-Detection AST SystemDetermines turbidity (growthin well), MIC, andsusceptibility (does microbegrow at drug concentrationcutoff?). Autonomouslymonitors bacteria duringincubation. Prototype costsunder $500. Utilizes 3D-printed housing and off-the-shelf components.
Image Processing and Deep LearningProcess:
1. Cameras capture images every 5 minutes2. Fiber intensities extracted from raw images3. Neural network uses fiber intensities over time to determine turbidity
One network for allwells/drugs/concentrations. Important forreal-world use, e.g.drugs, and conditions.Training only needs tobe performed oncebefore model can beapplied to real-worldbacterial growth.Dropout and batchnormalization.
ConclusionOur device performs early, autonomous AST in a cost-effective manner. Captures images duringincubation and aligns with clinical workflow. $500 for all components/electronics vs. $100,000+for commercial AST readers. Device surpasses FDA criteria (EA, CA, and errors) for automatedAST. In the future, will determine optimal number of fibers, test on different bacteria (Gramnegative), investigate temperature gradients within incubator
Figure 2 How antibiotic
resistance happens in a
patient’s body.
Causes of Antimicrobial Resistance• Natural processes• Overprescription/overuse of antibiotics• Livestock industry• Lack of new drugs
Readout Problems1. Manual reading by trained diagnostician. Inefficient use
of diagnostician’s time. Potentially dangerous to patientbecause requires up to 24 incubation for visible growth.Meanwhile, patient placed on harmful broad-spectrumantibiotics.
2. Automatic reading by commercial AST machines.Expensive ($100,000+). Space-inefficient.
Figure 5Image processing
Figure 6. Network turbidity predictions.
Essential
Agreement
Categorical
AgreementMajor Errors Very Major Errors
Avg time to meet (h) 6.13 6.98 4.02 9.39FDA Criteria > 90% > 90% < 3% < 3%
Figure 7System correctly detects turbidity in blindly-tested clinical Staphylococcus aureusplates after an average of just 5.72 h (compared to 18 h gold standard). Turbidity was correctly identified in 95.03% of cases. cross-validation data.
Figure 4 Antimicrobial susceptibility
testing system
Figure 8Neural network shows improved performance compared to logistic regression and threshold-based methods.
Figure 3 Typical plate, manual reading, and
automated reading
