Researchers have shown the effectiveness of an artificial intelligence (AI) system — known as a Convoluted Neural Network — to classify mosquito sex, genus, species, and strain.
Rapid and accurate identification of mosquitoes that transmit human pathogens such as malaria is an essential part of mosquito-borne disease surveillance, the study published in PLOS Neglected Tropical Diseases, reported.
Human malaria is an ongoing public health crisis affecting many continents, with the highest numbers of cases and people at risk in sub-Saharan Africa.
However, the identification of mosquitoes that transmit malaria can be difficult — some species are nearly indistinguishable even to trained taxonomists.
In the new work, the research team from the University of Rhode Island in the US, and colleagues applied a Convoluted Neural Network (CNN) to a library of 1,709 two-dimensional images of adult mosquitoes.
The mosquitoes were collected from 16 colonies in five geographic regions and included one species not readily identifiable to trained medical entomologists.
They also included mosquitoes that had been stored in two different ways — by flash freezing or as dried samples.
Using the library of identified species, the researchers trained the CNN to distinguish Anopheles from other mosquito genera, to identify species and sex within Anopheles, and to identify two strains within a single species.
They found a 99.96 percent prediction accuracy for class and a 98.48 percent accuracy for sex.
“These results demonstrate that image classification with deep learning can be a useful method for malaria mosquito identification, even among species with cryptic morphological variation,” the researchers said.
“The development of an independent and accurate method of species identification can potentially improve mosquito surveillance practices,” they noted.
