Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning

Lab on a Chip ◽  
2015 ◽  
Vol 15 (5) ◽  
pp. 1284-1293 ◽  
Author(s):  
Hatice Ceylan Koydemir ◽  
Zoltan Gorocs ◽  
Derek Tseng ◽  
Bingen Cortazar ◽  
Steve Feng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mobile Phone ◽  
Water Samples ◽  
Giardia Lamblia ◽  
Fluorescent Microscopy ◽  
Rapid Imaging ◽  
Fluorescent Microscope ◽  
Imaging Detection ◽  
Detection And Quantification

We report a mobile-phone based fluorescent microscope that uses machine learning to rapidly image, detect and quantify Giardia lamblia cysts in water samples.

Rapid imaging, detection, and quantification of Nosema ceranae spores in honey bees using mobile phone-based fluorescence microscopy

Lab on a Chip ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 789-797 ◽  
Author(s):  
Jonathan W. Snow ◽  
Hatice Ceylan Koydemir ◽  
Doruk Kerim Karinca ◽  
Kyle Liang ◽  
Derek Tseng ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Mobile Phone ◽  
Honey Bees ◽  
Nosema Ceranae ◽  
Rapid Imaging ◽  
Imaging Detection ◽  
Detection And Quantification

Nosema ceranae detection using a mobile phone.

Deep Learning-enabled Holographic Imaging Flow-Cytometry for Label-Free Detection of Giardia Lamblia in Water Samples

2021 ◽  
Author(s):  
Zoltán Göröcs ◽  
David Baum ◽  
Fang Song ◽  
Kevin de Haan ◽  
Hatice Ceylan Koydemir ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Deep Learning ◽  
Water Samples ◽  
Giardia Lamblia ◽  
Label Free ◽  
Holographic Imaging ◽  
Imaging Flow Cytometry ◽  
Label Free Detection

Mobile-phone-based colourimetric analysis for determining nitrite content in water

2018 ◽  
Vol 15 (7) ◽  
pp. 403 ◽  
Author(s):  
Chanida Puangpila ◽  
Jaroon Jakmunee ◽  
Somkid Pencharee ◽  
Wipada Pensrisirikul
Keyword(s):  
Mobile Phone ◽  
Water Samples ◽  
Diazonium Salt ◽  
Field Measurements ◽  
Cost Effective ◽  
Environmental Water ◽  
Sulfanilic Acid ◽  
Calibration Graph ◽  
Nitrite Ion ◽  
Griess Reaction

Environmental contextA widespread pollutant in groundwater, rivers and lakes is nitrite, which is commonly determined batchwise by using colourimetry. The batchwise method, however, requires relatively large and expensive instrumentation, and hence is unsuitable for in-field measurements. This work introduces a simple and portable colourimetric analyser based on a mobile-phone camera for monitoring nitrite concentrations in environmental water samples. AbstractA cost-effective and portable colourimetric analyser installed on a mobile phone was used to measure nitrite in water samples in Chiang Mai City, Thailand. The colourimetric detection was based on the Griess reaction, in which nitrite ion reacts with sulfanilic acid under acidic conditions to produce a diazonium salt that further reacts with N-(1-naphthyl)-ethylenediamine dihydrochloride to form a red–violet azo dye. Under controlled conditions using a light-tight box with LED flash lights, images of the red–violet solution were captured using a built-in camera and further analysed by a program, Panalysis, on the mobile phone. The calibration graph was created by measuring the red colour intensity of a series of standard nitrite solutions from 0.09–1.8 mg N L−1. The calibration equation was then automatically stored for nitrite analysis. The results demonstrated good performance of the mobile phone analyser as an analytical instrument. The accuracy (RE <4%) and precision (RSD ≤ 1%, intra- and inter-day) were also obtained with a detection limit of 0.03 mg N L−1 and a sample throughput of 40 samples per hour. Our results establish this simple, inexpensive and portable device as a reliable in-field monitor of nitrite in environmental waters.

scholarly journals Post hoc support vector machine learning for impedimetric biosensors based on weak protein–ligand interactions

The Analyst ◽  
2018 ◽  
Vol 143 (9) ◽  
pp. 2066-2075 ◽  
Author(s):  
Y. Rong ◽  
A. V. Padron ◽  
K. J. Hagerty ◽  
N. Nelson ◽  
S. Chi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Mobile Phone ◽  
Learning Algorithm ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Impedimetric Biosensor ◽  
Post Hoc

We develop a simple, open source machine learning algorithm for analyzing impedimetric biosensor data using a mobile phone.

Open source column: Deep learning in the browser

2019 ◽  
Vol 11 (1) ◽  
pp. 1-1
Author(s):  
Sabrina Kletz ◽  
Marco Bertini ◽  
Mathias Lux
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mobile Phone ◽  
Open Source ◽  
Web Pages ◽  
Web Browsers ◽  
Open Source Framework ◽  
Interesting Approach ◽  
Cross Platform ◽  
Client Device

Having already discussed MatConvNet and Keras, let us continue with an open source framework for deep learning, which takes a new and interesting approach. TensorFlow.js is not only providing deep learning for JavaScript developers, but it's also making applications of deep learning available in the WebGL enabled web browsers, or more specifically, Chrome, Chromium-based browsers, Safari and Firefox. Recently node.js support has been added, so TensorFlow.js can be used to directly control TensorFlow without the browser. TensorFlow.js is easy to install. As soon as a browser is installed one is ready to go. Browser based, cross platform applications, e.g. running with Electron, can also make use of TensorFlow.js without an additional install. The performance, however, depends on the browser the client is running, and memory and GPU on the client device. More specifically, one cannot expect to analyze 4K videos on a mobile phone in real time. While it's easy to install, and it's easy to develop based on TensorFlow.js, there are drawbacks: (i) developers have less control over where the machine learning actually takes place (e.g. on CPU or GPU), that it is running in the same sandbox as all web pages in the browser do, and (ii) that in the current release it still has rough edges and is not considered stable enough to use in production.

scholarly journals Light Emitting Diode (LED) Fluorescent Microscopy: a Milestone in the Detection of Paucibacillary Mycobacterium in Case of Pulmonary Tuberculosis

1970 ◽  
Vol 40 (1) ◽  
pp. 22-26 ◽  
Author(s):  
Z Khatun ◽  
MS Hossain ◽  
CK Roy ◽  
T Sultana ◽  
MQ Rahman ◽  
...  
Keyword(s):  
Pulmonary Tuberculosis ◽  
Hiv Risk ◽  
Gold Standard ◽  
Risk Evaluation ◽  
Financial Constraints ◽  
Light Emitting Diode ◽  
Fluorescent Microscopy ◽  
Light Emitting ◽  
Fluorescent Microscope ◽  
Lowenstein Jensen

In Bangladesh with a large number of pulmonary tuberculosis cases and financial constraints with high HIV risk, evaluation of scanty i.e paucibacillary cases has great importance. To study the efficacy of Light Emitting Diode fluorescent microscopy in the diagnosis of pulmonary tuberculosis specially paucibacillary cases in comparison to conventional fluorescent microscopy, Ziehl-Neelsen staining and culture of sputum samples from patients suspected of pulmonary tuberculosis. 150 sputum samples collected from the patients suspected of pulmonary tuberculosis were processed by the Petroff's method, and subjected to Ziehl-Neelsen staining (ZN), which were examined by both LED and conventional fluorescent microscope (CFM) and culture on Lowenstein- Jensen media (gold standard) for detection of Mycobacterium tuberculosis. In this study, out of 150 patients 14.67%, 8.67%, 4% cases were detected as paucibacillary (Scanty) cases by LED, CFM, ZN respectively. LED fluorescent microscopy is more effective in the detection of paucibacillary cases of pulmonary tuberculosis than other methods of microscopic examination. DOI: http://dx.doi.org/10.3329/bmj.v40i1.9958 BMJ 2011; 40(1): 22-26

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