Why develop something like that?

Because, as Javed et al. report, “Aedes aegypti mosquitos are the primary vector of several medically significant viruses, including dengue, Zika, yellow fever, and chikungunya.” And although there has been plenty of research into how to limit or even eliminate mosquito-transmitted pathogens, these diseases are still a major problem all over the world. What’s more, in the future they may even become a bigger concern, as climate change worsens and we become more interconnected. 

But still, why the eggs specifically?

Because researchers can actually glean a lot of information just from them. For example, the number of eggs from can be used to estimate the mosquito population in a region. Additionally, certain pathogens can actually change the behavior of the mosquitoes, including their fecundity (aka, how many eggs they lay).

However, as you might imagine, manually counting tiny mosquito eggs can be quite difficult, especially as samples can have high levels of eggs. That’s where the software comes in.

Overall, EggCount AI performed remarkably well. For micro images, it had an accuracy of 98.88%, and for macro images, 96.06%, outperforming similar tools. As Javed et al. report, by producing accurate egg counts, it can lead researchers “to a deeper understanding of mosquito behaviour and fitness traits.” What’s more, for anyone curious about the tool, EggCount AI is free and available on the article page. 

So, what does the future hold for AI for science? It’s tough to say for sure, but clearly it will play a large role moving forward for research. And that’s why Parasites & Vectors recently launched a new article collection, Artificial intelligence, parasites, and parasitic diseases, welcoming articles reporting on the uses of AI and deep learning in parasitology research, such as EggCount AI. We encourage anyone interested in AI applications in science to check in periodically as more papers are added to the collection.