{"id":9449,"date":"2021-05-05T09:33:16","date_gmt":"2021-05-05T07:33:16","guid":{"rendered":"https:\/\/www.ceab.csic.es\/artificial-intelligence-identifies-the-tiger-mosquito-from-the-photos-received-in-the-mosquito-alert-app\/"},"modified":"2021-05-05T09:33:16","modified_gmt":"2021-05-05T07:33:16","slug":"artificial-intelligence-identifies-the-tiger-mosquito-from-the-photos-received-in-the-mosquito-alert-app","status":"publish","type":"post","link":"https:\/\/www.ceab.csic.es\/en\/artificial-intelligence-identifies-the-tiger-mosquito-from-the-photos-received-in-the-mosquito-alert-app\/","title":{"rendered":"Artificial intelligence identifies the tiger mosquito from the photos received in the Mosquito Alert app"},"content":{"rendered":"

Researchers from Mosquito\u00a0Alert\u00a0<\/a> (who belong to CEAB-CSIC<\/a>,<\/strong> CREAF<\/a>\u00a0and UPF<\/a>)\u00a0together with researchers from the\u00a0University of Budapest<\/a>\u00a0have shown that an artificial intelligence algorithm is capable of recognizing the tiger mosquito (Aedes albopictus<\/em>) in the photos sent by Mosquito Alert users. The results of the study\u00a0published in\u00a0Scientific Reports<\/em>\u00a0<\/a>have been obtained by applying deep learning technology or deep learning, an aspect of artificial intelligence that seeks to emulate the way of learning of humans and that has previously been used in the health field to interpret medical images (X-rays of patients with COVID19 to detect pneumonia, or facial features to detect heart disease, among others).<\/p>\n

Deep learning needs a lot of training data for the machine to learn. In the case of the Mosquito Alert app, these images have been sent by the public and labeled by the project experts as \u201ctiger mosquito\u201d or \u201cno tiger mosquito\u201d for years. Specifically, the study used 7,168 classified photographs of mosquitoes that the project participants had sent between 2015 and 2019. After training, the algorithm has been able to correctly classify 96% of the photographs of this insect.<\/p>\n

\u201cThe initial idea is to get the machine to classify the simplest photos, and leave the task of identifying the most problematic images that require consensus to the experts. As the artificial system learns from the classifications of the experts, we will be able to expand the range of automatically cataloged species \u201d, explains\u00a0John Palmer<\/strong>, UPF researcher and co-director of Mosquito Alert.<\/p><\/blockquote>\n

<\/h3>\n
\"
Figure 1. Schematic figure of the labeling process. Participants usually upload several images in a single report. The best photo is picked by the validator who first marks the harassing or non-appropriate photos as hidden. All the non-best photos are marked as not classified. In some rare events, two or three images are annotated from the same report.<\/figcaption><\/figure>\n

More predictability<\/h3>\n

This milestone can mark a before and after in the surveillance and monitoring of the tiger mosquito and other mosquitoes capable of transmitting diseases.<\/p>\n

\u201cWe are training a social immune system against these mosquitoes. The faster the threat is detected, the faster it can be acted upon \u201d, comments\u00a0Frederic Bartumeus<\/strong>, co-director of Mosquito Alert and ICREA researcher at CEAB-CSIC and CREAF.<\/p><\/blockquote>\n

On the one hand, the citizen science of Mosquito Alert allows anyone to be part of this new social immune system and contribute a massive number of photos of mosquitoes, on the other, artificial intelligence allows, to accelerate the classification process of the received photos and thus help public health experts make better and faster decisions about mosquito management.<\/p>\n

\u201cIn times of greatest need, such as in the months of greatest mosquito activity or in a context of epidemiological crisis, artificial intelligence can help us so that the system can absorb a greater amount of information, controlling its quality at all times, which it is key if the data is to be used for decision-making in public health \u201d, adds Frederic Bartumeus.<\/strong><\/p><\/blockquote>\n

Automating saves lives<\/h3>\n

The presence of the tiger mosquito in Spain poses a threat to public health. Millions of people are affected by its presence and are exposed to the risk of transmitting diseases such as dengue or chikungunya.\u00a0In Europe, the tiger mosquito has been implicated every year since 2007 in small locally transmitted outbreaks of these viral diseases for which no vaccines are available<\/strong>. The only preventive measure is to control the mosquitoes that transmit them. Assessing the risk and the necessary action measures to mitigate it requires having accurate information on tiger mosquito populations, a costly and laborious task that requires manual placement and inspection of traps and their subsequent analysis in the laboratory where the insects are identified. A methodology that is not feasible to cover large geographic areas.<\/p>\n

\"
Figure 2. Geographical diversity of the submitted images based on the provided geolocation tags. It can be clearly seen that while the vast majority of the Mosquito Alert participants are based in Europe (particularly Spain), images were taken all over the world.<\/figcaption><\/figure>\n

Mosquito Alert\u2019s citizen science methods, which allow anyone to report the presence of a mosquito through a mobile application available on Android and iOS, is an alternative that makes it easy to cover large geographic areas throughout the mosquito season. Since 2015, the initiative receives thousands of photographs every year that help estimate the abundance of mosquitoes. However, this large volume of photographs continues to be classified by visual examination by expert entomologists, a task that requires time and years of experience. Integrating artificial intelligence into this process can speed up classification and thus develop near-real-time hazard maps that improve tiger mosquito management.<\/p>\n

\u201cIt will take time until a machine can have the same capacity as an expert eye, especially for other less characteristic species than the tiger mosquito. In Spain, 62 species of mosquitoes have been described, many of which currently cannot be classified from an image, but must be examined under a microscope. In some other cases, even a genetic analysis is required to identify them \u201d, explain Roger Eritja<\/strong>, CREAF scientist and Mosquito Alert entomological director.<\/p>\n

\n

 <\/p>\n

Reference rticle:<\/strong><\/p>\n

Pataki BA, Garriga J, Eritja R, Palmer JRB, Bartumeus F, Csabai I. 2021.\u00a0Deep learning identification for citizen science surveillance of tiger mosquitoes.<\/a>\u00a0Scientific Reports<\/em>\u00a011: 4718<\/p>\n","protected":false},"excerpt":{"rendered":"

Researchers from Mosquito\u00a0Alert\u00a0 (who belong to CEAB-CSIC, CREAF\u00a0and UPF)\u00a0together with researchers from the\u00a0University of Budapest\u00a0have shown that an artificial intelligence algorithm is capable of recognizing the tiger mosquito (Aedes albopictus) in the photos sent by Mosquito Alert users. The results of the study\u00a0published in\u00a0Scientific Reports\u00a0have been obtained by applying deep learning technology or deep learning,… Artificial intelligence identifies the tiger mosquito from the photos received in the Mosquito Alert app<\/span><\/a><\/p>\n","protected":false},"author":4,"featured_media":8735,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[334],"tags":[490,77,716,1094,113,124,1095],"class_list":["post-9449","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-app-mosquito-alert","tag-ciencia-ciutadana","tag-citizen-science","tag-deep-learning","tag-intelligencia-artificial","tag-mosquit-tigre","tag-tiger-mosquito"],"acf":[],"yoast_head":"\nArtificial intelligence identifies the tiger mosquito from the photos received in the Mosquito Alert app | CEAB<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ceab.csic.es\/en\/artificial-intelligence-identifies-the-tiger-mosquito-from-the-photos-received-in-the-mosquito-alert-app\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Artificial intelligence identifies the tiger mosquito from the photos received in the Mosquito Alert app | CEAB\" \/>\n<meta property=\"og:description\" content=\"Researchers from Mosquito\u00a0Alert\u00a0 (who belong to CEAB-CSIC, CREAF\u00a0and UPF)\u00a0together with researchers from the\u00a0University of Budapest\u00a0have shown that an artificial intelligence algorithm is capable of recognizing the tiger mosquito (Aedes albopictus) in the photos sent by Mosquito Alert users. 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