Automated Tuberculosis Diagnosis Using Fluorescence Images from a Mobile Microscope

Chang, J. Morris; Arbeláez, Pablo; Switz, Neil A.; Reber, Clay; Tapley, Asa; Davis, J. Lucian; Cattamanchi, Adithya; Fletcher, Daniel A.; Malik, Jitendra

doi:10.1007/978-3-642-33454-2_43

Cited by 54 publications

(56 citation statements)

References 12 publications

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“…Indeed, mobile phone coverage is extensive in most low-income countries in which TB is endemic (32)(33)(34), and CellScope images can also be easily stored for later transmission when a mobile network is unavailable. In addition, significant progress has been made in the development of reliable computer algorithms for the detection of AFB in digital images of sputum smears (11,35), including one algorithm based on CellScope images that performed as well as human readers (36). Automated image analysis may facilitate onthe-spot diagnosis, improve sensitivity by enabling analysis of a larger number of fields than typically evaluated by a human microscopist, and improve specificity through rigid and reliable criteria for AFB identification.…”

Section: Discussionmentioning

confidence: 99%

Mobile Digital Fluorescence Microscopy for Diagnosis of Tuberculosis

et al. 2013

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Access to sputum smear microscopy in high-tuberculosis (TB)-burden regions is limited by a scarcity of microscopes and experienced technicians. We evaluated the accuracy of CellScope, a novel digital fluorescence microscope that may expand access to microscopy. The study utilized smear microscopy slides prepared from sputum specimens submitted by consecutive adults with >2 weeks of cough who were admitted to Mulago Hospital (Kampala, Uganda). Conventional light-emitting diode (LED) fluorescence microscopy (FM) and mycobacterial culture were performed by experienced technicians. Two U.S.-based postgraduate researchers without prior microscopy experience restained, imaged, and interpreted the slides using CellScope. We assessed whether sensitivity and specificity of CellScope-based LED FM was noninferior to conventional LED FM by using a preselected margin of inferiority of 15%. Of 525 patients included, 72% were HIV seropositive and 39% had culture-confirmed TB. The proportions of positive results were similar with CellScope and conventional LED FM (34% versus 32%, respectively; P ‫؍‬ 0.32), and agreement was substantial. CellScope accuracy was within the noninferiority margin for both sensitivity (63% versus 70%; difference, ؊7%; 95% confidence interval [CI], ؊13% to ؊1%) and specificity (85% versus 92%; difference, ؊7%; 95% CI, ؊12% to ؊3%). A subanalysis of 43 slides evaluated by each CellScope reader found substantial interreader reliability (customweighted kappa, 0.65) and variable intrareader reliability (custom-weighted kappa, 0.11 versus 0.48). CellScope offers promise for expanding microscopy services. Future studies should evaluate the device when operated by health workers in low-resource settings, the feasibility of image transmission and analysis by experienced microscopists, and the accuracy of automated image analysis algorithms.T uberculosis (TB) continues to be responsible for more deaths than any other infectious disease besides HIV/AIDS (1). In Africa, where the burden of TB is greatest, approximately 40% of individuals who fall ill with the disease go undiagnosed (2). Sputum smear microscopy is capable of detecting the majority of infectious TB cases, and mathematical models suggest that expanding access to high-quality smear microscopy may improve individual outcomes (3) and reduce TB prevalence and incidence (4). Although recently developed molecular detection methods are becoming available in some diagnostic centers (5), the costs and infrastructure requirements of current tests are prohibitive for most peripheral clinics in high-burden countries (6). Therefore, efforts to improve the quality and expand the reach of microscopy continue to be a global priority (7).In low-income countries, smear microscopy typically involves direct visualization of stained smears by experienced laboratory technicians using conventional light microscopes. In contrast, in high-income countries, microscopy in the related discipline of pathology increasingly involves the use of digital images viewed on high-resol...

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Section: Discussionmentioning

confidence: 99%

Mobile Digital Fluorescence Microscopy for Diagnosis of Tuberculosis

et al. 2013

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“…In Chang et al (2012b), a CBMIA system is proposed to distinguish tuberculosis from other objects, where morphological operations, Gaussian kernel based template matching, global and local shape feature extraction methods [Hu moments, geometric, photometric and Histogram of Orientation Gradient (HOG) features], and SVM classifier design are applied. In the experiment, 594 images are tested, and an average classification accuracy around 90% is achieved.…”

Section: Overview Of MM Classificationmentioning

confidence: 99%

“…Beneficial AMs can help farmers to increase the agricultural yields, for Shabtai et al (1996), Ronen et al (2002) IM 1998 Gerlach SR et al Gerlach et al (1998) IM 2001 Lee MS et al Lee et al (2001) IM 2002 Lomander A et al Lomander et al (2002) IM 2002 Park JP et al Park et al (2002) IM 2007 Lecault V et al Lecault et al (2007) IM 2011 Yu B et al Yu et al (2011) MM 1992 Reichl U et al Reichl et al (1992) MM 1994 Pichon D et al Pichon et al (1994) MM 1996 Oh B et al Oh et al (1996) MM 1997 Tamura S et al Tamura et al (1997) MM 1998 Veropoulos K et al Veropoulos et al (1998) MM 1998 Wit P et al Wit and Busscher (1998) MM 1999 Kay JW et al Kay et al (1999) MM 2000 Khutlang R et al Khutlang et al (2009Khutlang et al ( , 2010a MM 2011 Rulaningtyas R et al Rulaningtyas et al (2011) MM 2011 Pangilinan C et al Pangilinan et al (2011) MM 2011-2012Osman MK et al Osman et al (2011a, b, 2012 MM 2012 Chang J et al Chang et al (2012b) MM 2012-2016 Priya E et al Priya et al (2012), Srinivasan (2015a, b, 2016) Culverhouse et al (1994Culverhouse et al ( , 1996Culverhouse et al ( , 2000Culverhouse et al ( , 2003Culverhouse et al ( , 2006a WM 1995 Thiel S et al Thiel and Davies (1995), …”

Section: Application Domain Introductionmentioning

confidence: 99%

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Wang

2017

Artif Intell Rev

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Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semior full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future devel-

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“…This platform allowed blood, urine, sputum, or water analysis, including the examination of white blood cells from whole blood samples and Giardia Lamblia cysts inspection from water samples. Additionally, CellScope devices can be adopted into a fluorescent imaging system for clinical tuberculosis (TB) diagnosis [49][50][51]. Recently, a miniaturized fluorescence microscope coupled with a CMOS sensor system has been applied to sophisticated neuroscience research [52].…”

Section: Cellphone-based Fluorescent Microscopymentioning

confidence: 99%