“In 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 ”, adds Frederic Bartumeus. 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. The faster the threat is detected, the faster it can be acted upon ”, comments Frederic Bartumeus, co-director of Mosquito Alert and ICREA researcher at CEAB-CSIC and CREAF. “We are training a social immune system against these mosquitoes. This milestone can mark a before and after in the surveillance and monitoring of the tiger mosquito and other mosquitoes capable of transmitting diseases. As the artificial system learns from the classifications of the experts, we will be able to expand the range of automatically cataloged species ”, explains John Palmer, UPF researcher and co-director of Mosquito Alert. “The 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. After training, the algorithm has been able to correctly classify 96% of the photographs of this insect. Specifically, the study used 7,168 classified photographs of mosquitoes that the project participants had sent between 20. In the case of the Mosquito Alert app, these images have been sent by the public and labeled by the project experts as “tiger mosquito” or “no tiger mosquito” for years. John Palmer: "The 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". Deep learning needs a lot of training data for the machine to learn.
The results of the study published in Scientific Reports 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). Researchers from Mosquito Alert (who belong to CEAB-CSIC, CREAF and UPF) together with researchers from the University of Budapest have 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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