Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools : A review
Rubio Maturana, Carles (Hospital Universitari Vall d'Hebron)
de Oliveira, Allisson Dantas (Universitat Politècnica de Catalunya)
Nadal, Sergi (Universitat Politècnica de Catalunya)
Bilalli, Besim (Universitat Politècnica de Catalunya)
Serrat, Francesc Zarzuela (Hospital Universitari Vall d'Hebron)
Espasa, Mateu (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))
Sulleiro Igual, Elena (Hospital Universitari Vall d'Hebron)
Bosch, Mercedes (Probitas Foundation)
Lluch, Anna Veiga (Probitas Foundation)
Abelló, Alberto (Universitat Politècnica de Catalunya)
López-Codina, Daniel (Universitat Politècnica de Catalunya)
Pumarola Suñé, Tomàs (Hospital Universitari Vall d'Hebron)
Clols, Elisa Sayrol (Universitat Politècnica de Catalunya)
Joseph-Munné, Joan (Hospital Universitari Vall d'Hebron)
Universitat Autònoma de Barcelona

Data:	2022
Resum:	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.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Artificial intelligence ; Deep learning ; Digital imaging techniques ; Malaria ; Malaria diagnosis ; Microscopic examination ; Smartphone application
Publicat a:	Frontiers in microbiology, Vol. 13 (november 2022) , ISSN 1664-302X

DOI: 10.3389/fmicb.2022.1006659
PMID: 36458185

17 p, 1.8 MB

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