AUTHOR=Maturana Carles Rubio , de Oliveira Allisson Dantas , Nadal Sergi , Bilalli Besim , Serrat Francesc Zarzuela , Soley Mateu Espasa , Igual Elena Sulleiro , Bosch Mercedes , Lluch Anna Veiga , Abelló Alberto , López-Codina Daniel , Suñé Tomàs Pumarola , Clols Elisa Sayrol , Joseph-Munné Joan TITLE=Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review JOURNAL=Frontiers in Microbiology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.1006659 DOI=10.3389/fmicb.2022.1006659 ISSN=1664-302X ABSTRACT=

Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.