The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria Diagnosis.
The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria Diagnosis.
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DOI：
10.2196/resprot.6758
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发表时间：
2017-04-25
影响因子：
1.7
通讯作者：
Albuquerque J
中科院分区：
文献类型：
--
作者：
Oliveira AD;Prats C;Espasa M;Zarzuela Serrat F;Montañola Sales C;Silgado A;Codina DL;Arruda ME;I Prat JG;Albuquerque J
疟疾是影响全球偏远地区的公共卫生问题。气候变化导致按蚊在以前无人居住的地区得以生存，从而加剧了这一问题。因此，一些组织已将开发用于疟疾自动诊断的新闻系统作为优先事项。这项研究的目的是开发一种新的、自动化的、基于移动设备的疟疾诊断系统。该系统使用吉姆萨染色的外周血样本结合光学显微镜来识别处于环状发育阶段的恶性疟原虫种类。该系统使用图像处理和人工智能技术以及已知的面部检测算法来识别疟原虫寄生虫。该算法基于积分图像和类哈尔特征概念，并利用具有自适应增强学习的弱分类器。通过去除血细胞周围的背景，在预处理步骤中减小了学习算法的搜索范围。作为概念验证实验，该工具用于先前制作的 555 张疟疾阳性和 777 张疟疾阴性幻灯片。该系统的平均准确度为 91%，这意味着每 100 个寄生虫感染样本中，有 91 个被正确识别。资源匮乏国家的可及性障碍可以通过低成本诊断工具来解决。我们的系统是为移动设备（手机和平板电脑）开发的，通过允许访问偏远社区的卫生中心来解决这个问题，重要的是，不依赖于广泛的疟疾专业知识或昂贵的诊断检测设备。
Malaria is a public health problem that affects remote areas worldwide. Climate change has contributed to the problem by allowing for the survival of Anopheles in previously uninhabited areas. As such, several groups have made developing news systems for the automated diagnosis of malaria a priority. The objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. The system uses Giemsa-stained peripheral blood samples combined with light microscopy to identify the Plasmodium falciparum species in the ring stage of development. The system uses image processing and artificial intelligence techniques as well as a known face detection algorithm to identify Plasmodium parasites. The algorithm is based on integral image and haar-like features concepts, and makes use of weak classifiers with adaptive boosting learning. The search scope of the learning algorithm is reduced in the preprocessing step by removing the background around blood cells. As a proof of concept experiment, the tool was used on 555 malaria-positive and 777 malaria-negative previously-made slides. The accuracy of the system was, on average, 91%, meaning that for every 100 parasite-infected samples, 91 were identified correctly. Accessibility barriers of low-resource countries can be addressed with low-cost diagnostic tools. Our system, developed for mobile devices (mobile phones and tablets), addresses this by enabling access to health centers in remote communities, and importantly, not depending on extensive malaria expertise or expensive diagnostic detection equipment.