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Researchers develop an image recognition tool to identify mosquitoes in the field, and a new paper highlights the nuanced realities of malaria decision-making in endemic regions.
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New research has demonstrated that Artificial Intelligence (AI) can be applied to malaria surveillance, helping identify the sex, genus, species and strain of mosquitoes in the field. Using a library of mosquito images, researchers developed a Convolutional Neural Network – an image recognition tool – to identify mosquitoes. The results were promising, with around 97% accurate species identification, and 98% sex identification. The research has application to surveillance: understanding the movement and density of mosquitoes in the field, and is particularly relevant to mosquitoes that are not easily distinguishable morphologically, even by highly trained entomologists, like the Anopheles gambiae complex.
A new paper has highlighted the nuanced realities of malaria control decision-making in endemic regions. The researchers show that National Malaria Control Programs (NMCPs) are not only driven by technical factors but a wide range of political and economic factors, too.
Delimiting Cryptic Morphological Variation Among Human Malaria Vector Species Using Convolutional Neural Networks
Competing Interests, Clashing Ideas and Institutionalizing Influence: Insights Into the Political Economy of Malaria Control From Seven African Countries
Image Credits: Couret et al. [PLOS Article]
Scientific Advisor: Katharine Collins, Radboud University Medical Centre
The post Researchers Use AI to Identify Morphologically Similar Mosquitoes appeared first on The Fight Malaria Blog.