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Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review
dc.contributor.author | Rubio Maturana, Carles |
dc.contributor.author | Oliveira, Allisson Dantas de |
dc.contributor.author | Nadal Francesch, Sergi |
dc.contributor.author | Bilalli, Besim |
dc.contributor.author | Abelló Gamazo, Alberto |
dc.contributor.author | López Codina, Daniel |
dc.contributor.author | Sayrol Clols, Elisa |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Física |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació |
dc.date.accessioned | 2023-01-24T10:54:29Z |
dc.date.available | 2023-01-24T10:54:29Z |
dc.date.issued | 2022-11-15 |
dc.identifier.citation | Rubio, 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. |
dc.identifier.issn | 1664-302X |
dc.identifier.uri | http://hdl.handle.net/2117/380979 |
dc.description.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. |
dc.description.sponsorship | The project is funded by the Microbiology Department of Vall d’Hebron Universitary Hospital, the Cooperation Centre of the Universitat Politècnica de Catalunya (CCD-UPC) and the Probitas Foundation |
dc.format.extent | 17 p. |
dc.language.iso | eng |
dc.publisher | Frontiers Media SA |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi numèrica::Modelització matemàtica |
dc.subject.lcsh | Malaria--Diagnosis |
dc.subject.other | Malaria diagnosis |
dc.subject.other | Digital imaging techniques |
dc.subject.other | Deep learning |
dc.subject.other | Artificial intelligence |
dc.subject.other | Microscopic examination |
dc.subject.other | Smartphone application |
dc.subject.other | Malaria |
dc.title | Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review |
dc.type | Article |
dc.subject.lemac | Malària |
dc.contributor.group | Universitat Politècnica de Catalunya. BIOCOM-SC - Biologia Computacional i Sistemes Complexos |
dc.contributor.group | Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Services, Information and Data Engineering |
dc.identifier.doi | 10.3389/fmicb.2022.1006659 |
dc.relation.publisherversion | https://www.frontiersin.org/articles/10.3389/fmicb.2022.1006659/full |
dc.rights.access | Open Access |
local.identifier.drac | 34879903 |
dc.description.version | Postprint (published version) |
local.citation.author | Rubio, C.; AD, Oliveira; Nadal, S.; Bilalli, B.; Abello, A.; Lopez, D.; Sayrol, E. |
local.citation.publicationName | Frontiers in microbiology |
local.citation.volume | 13 |
local.citation.number | 13:1006659 |
local.citation.startingPage | 1 |
local.citation.endingPage | 17 |
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