Department of Food Chemistry, Institute of Nutritional Science, University of Potsdam, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany.
Food Chemistry, Faculty of Mathematics and Natural Sciences, University of Wuppertal, Gaußstraße 20, 42119 Wuppertal, Germany.
Sensors (Basel). 2019 Mar 26;19(6):1468. doi: 10.3390/s19061468.
The nematode 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 for toxicological trials. With the general applicability of the detection of from microscope images via machine learning, as well as of smartphone-based microscopes, this article investigates the suitability of smartphone-based microscopy to detect in a complete Petri dish. Thereby, the article introduces a smartphone-based microscope (including optics, lighting, and housing) for monitoring and the corresponding classification via a trained Histogram of Oriented Gradients (HOG) feature-based Support Vector Machine for the automatic detection of . Evaluation showed classification sensitivity of 0.90 and specificity of 0.85, and thereby confirms the general practicability of the chosen approach.
线虫通常被用作替代动物模型，因为它具有许多优点，例如可以直接在显微镜下观察到形态变化。该模型的局限性包括使用昂贵且繁琐的显微镜，以及限制了在毒理学试验中全面使用。随着通过机器学习从显微镜图像中检测线虫的普遍适用性，以及基于智能手机的显微镜的出现，本文研究了基于智能手机的显微镜在检测完整培养皿中的线虫方面的适用性。为此，本文介绍了一种基于智能手机的显微镜（包括光学器件、照明和外壳），用于监测线虫，并通过经过训练的基于方向梯度直方图（HOG）特征的支持向量机进行分类，以自动检测线虫。评估结果表明，分类的敏感性为 0.90，特异性为 0.85，从而证实了所选方法的普遍适用性。
