iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope

Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consumin...

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Detalles Bibliográficos
Autores: Rubio Maturana, Carles|||0000-0002-5615-9278, Oliveira, Allisson Dantas de|||0000-0002-8267-9760, Nadal Francesch, Sergi|||0000-0002-8565-952X, Zarzuela Serrat, Francesc, Sulleiro Igual, Elena, Ruiz Marti, Edurne, Bilalli, Besim|||0000-0002-0575-2389, Veiga Lluch, Anna, Espasa Soley, Mateu, Abelló Gamazo, Alberto|||0000-0002-3223-2186, Pumarola Sunyer, Tomas, Segu Estruch, Marta, López Codina, Daniel|||0000-0002-0408-4526, Sayrol Clos, Elisa, Joseph Munné, Joan
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/404932
Acceso en línea:https://hdl.handle.net/2117/404932
https://dx.doi.org/10.3389/fmicb.2023.1240936
Access Level:acceso abierto
Palabra clave:Malaria -- Diagnosis
Diagnostic Imaging--methods
Artificial intelligence
Medical technology
Malària
Diagnòstic per microscòpia
Intel·ligència artificial
Cooperació per al desenvolupament
Intel·ligència artificial--Aplicacions a la medicina
Malària--Diagnòstic
Microscòpia
Àrees temàtiques de la UPC::Ciències de la salut
Descripción
Sumario:Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it. The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.

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