Artificial Intelligence and Malaria

Malaria disease is due to the infection with Plasmodium parasites transmitted by a mosquito vector belonging to the genus Anopheles. To combat malaria, effective diagnosis and treatment using artemisinin-based combinations are needed, as well as strategies that are aimed at reducing or stopping transmission by mosquito vectors. Even if the conventional microscopic diagnosis is the gold standard for malaria diagnosis, it is time consuming, and the diagnostic performance depends on techniques and human expertise. In addition, tools for characterizing Anopheles vectors are limited and difficult to establish in the field. The advent of computational biology, information technology infrastructures, and mobile computing power offers the opportunity to use artificial intelligence (AI) approaches to address challenges and technical needs specific to malaria-endemic countries. This chapter illustrates the trends, advances, and future challenges linked to the deployment of AI in malaria. Two innovative AI approaches are described. The first is the image-based automatic classification of malaria parasites and vectors, and the second is the proteomics analysis of vectors. The developed applications are aimed at facilitating malaria diagnosis by performing malaria parasite detection, species identification, and estimation of parasitaemia. In the future, they can lead to efficient and accurate diagnostic tools, revolutionizing the urgent diagnosis of malaria. Other applications focus on the characterization of mosquito vectors by performing species identification, behavior, and biology descriptions. If field-validated, these promising approaches will facilitate the epidemiological monitoring of malaria vectors and saving resources by preventing or reducing malaria transmission.