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dc.contributor.authorCeylan Koydemir, Hatice
dc.contributor.authorFeng, Steve
dc.contributor.authorLiang, Kyle
dc.contributor.authorNadkarni, Rohan
dc.contributor.authorBenien, Parul
dc.contributor.authorOzcan, Aydogan
dc.date.accessioned2017-10-04T14:59:16Z
dc.date.available2017-10-04T14:59:16Z
dc.date.issued2017-06-14
dc.identifier.citationCeylan Koydemir H, Feng S, Liang K, Nadkarni R, Benien P, et al. (2017) Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy. Nanophotonics 6. Available: http://dx.doi.org/10.1515/nanoph-2017-0001.
dc.identifier.issn2192-8614
dc.identifier.issn2192-8606
dc.identifier.doi10.1515/nanoph-2017-0001
dc.identifier.urihttp://hdl.handle.net/10754/625787
dc.description.abstractGiardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of ~0.8 cm2 and weighs only ~180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging) approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond water) and achieved a limit of detection of 12 cysts per 10 ml, an average cyst capture efficiency of ~79%, and an accuracy of ~95%. Providing rapid detection and quantification of waterborne pathogens without the need for a microbiology expert, this field-portable imaging and sensing platform running on a smartphone could be very useful for water quality monitoring in resource-limited settings.
dc.description.sponsorshipThis project was funded by the Army Research Office (ARO). The Ozcan Research Group at UCLA gratefully acknowledges the support of the Presidential Early Career Award for Scientists and Engineers (PECASE), the ARO (W911NF-13-1-0419 and W911NF-13-1-0197), the ARO Life Sciences Division, the National Science Foundation (NSF) CBET Division Biophotonics Program, the NSF Emerging Frontiers in Research and Innovation (EFRI) Award, the NSF EAGER Award, NSF INSPIRE Award, NSF Partnerships for Innovation:Building Innovation Capacity (PFI:BIC) Program, Office of Naval Research (ONR), the National Institutes of Health (NIH), the Howard Hughes Medical Institute (HHMI), Vodafone Americas Foundation, the Mary Kay Foundation, Steven and Alexandra Cohen Foundation, and KAUST. This work is based upon research performed in a laboratory renovated by the NSF under Grant No. 0963183, which is an award funded under the American Recovery and Reinvestment Act of 2009 (ARRA).
dc.publisherWalter de Gruyter GmbH
dc.subjectmobile phone microscopy
dc.subjectmachine learning
dc.subjectlow resource settings
dc.subjectfluorescence microscopy
dc.subjectGiardia detection
dc.subjectwaterborne parasites
dc.titleComparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy
dc.typeArticle
dc.identifier.journalNanophotonics
dc.contributor.institutionDepartment of Electrical Engineering, University of California Los Angeles (UCLA), CA 90095, USA
dc.contributor.institutionDepartment of Computer Science and Engineering, University of California Los Angeles (UCLA), CA 90095, USA
dc.contributor.institutionDepartment of Bioengineering, University of California Los Angeles (UCLA), CA 90095, USA
dc.contributor.institutionCalifornia Nanosystems Institute (CNSI), University of California Los Angeles (UCLA), CA 90095, USA
dc.date.published-online2017-06-14
dc.date.published-print2017-01-27


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