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Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

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10.3389/fmicb.2022.1006659
 
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Rubio Maturana, CarlesMés informacióMés informació
Oliveira, Allisson Dantas deMés informacióMés informació
Nadal Francesch, SergiMés informacióMés informacióMés informació
Bilalli, BesimMés informacióMés informacióMés informació
Abelló Gamazo, AlbertoMés informacióMés informacióMés informació
López Codina, DanielMés informacióMés informacióMés informació
Sayrol Clols, ElisaMés informacióMés informació
Document typeArticle
Defense date2022-11-15
PublisherFrontiers Media SA
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 4.0 International
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 4.0 International
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 illion 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.
CitationRubio, C. [et al.]. Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review. "Frontiers in microbiology", 15 Novembre 2022, vol. 13, núm. 13:1006659, p. 1-17. 
URIhttp://hdl.handle.net/2117/380979
DOI10.3389/fmicb.2022.1006659
ISSN1664-302X
Publisher versionhttps://www.frontiersin.org/articles/10.3389/fmicb.2022.1006659/full
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