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.
A 17-year-old asymptomatic male from The Gambia presented for a routine health examination after migration to Spain. Laboratory diagnosis confirmed the presence of Loa loa microfilariae. This unusual finding emphasizes the importance of screening in newly arrived migrants and the need of an extended anamnesis including migratory route and previous travels.
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A four-year-old traveller returned from Senegal with symptoms compatible with malaria. Plasmodium falciparum HRP-2 antigen detection was negative while panmalarial aldolase was positive. Microscopy confirmed the presence of P. falciparum trophozoites with parasitaemia of 16%. Discarding other causes of false-negative antigen detection, this is a rare case of prozone effect.
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