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Article

Mass Surveilance of C. elegans—Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection

1
Department of Food Chemistry, Institute of Nutritional Science, University of Potsdam, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany
2
Food Chemistry, Faculty of Mathematics and Natural Sciences, University of Wuppertal, Gaußstraße 20, 42119 Wuppertal, Germany
3
Assistance Systems and Medical Device Technology, Department of Health Services Research, Faculty of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Ammerlaender Heerstr. 114-118, 26129 Oldenburg, Germany
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(6), 1468; https://doi.org/10.3390/s19061468
Received: 18 February 2019 / Revised: 17 March 2019 / Accepted: 20 March 2019 / Published: 26 March 2019
(This article belongs to the Special Issue Smartphone-Based Biosensing)
The nematode Caenorhabditis elegans (C. elegans) is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and restrictions of the comprehensive use of C. elegans for toxicological trials. With the general applicability of the detection of C. elegans from microscope images via machine learning, as well as of smartphone-based microscopes, this article investigates the suitability of smartphone-based microscopy to detect C. elegans in a complete Petri dish. Thereby, the article introduces a smartphone-based microscope (including optics, lighting, and housing) for monitoring C. elegans and the corresponding classification via a trained Histogram of Oriented Gradients (HOG) feature-based Support Vector Machine for the automatic detection of C. elegans. Evaluation showed classification sensitivity of 0.90 and specificity of 0.85, and thereby confirms the general practicability of the chosen approach. View Full-Text
Keywords: Caenorhabditis elegans; machine learning; smartphone; microscope; SVM; HOG Caenorhabditis elegans; machine learning; smartphone; microscope; SVM; HOG
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MDPI and ACS Style

Bornhorst, J.; Nustede, E.J.; Fudickar, S. Mass Surveilance of C. elegans—Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection. Sensors 2019, 19, 1468. https://doi.org/10.3390/s19061468

AMA Style

Bornhorst J, Nustede EJ, Fudickar S. Mass Surveilance of C. elegans—Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection. Sensors. 2019; 19(6):1468. https://doi.org/10.3390/s19061468

Chicago/Turabian Style

Bornhorst, Julia, Eike Jannik Nustede, and Sebastian Fudickar. 2019. "Mass Surveilance of C. elegans—Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection" Sensors 19, no. 6: 1468. https://doi.org/10.3390/s19061468

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