Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria

Carles Rubio Maturana, Allisson Dantas de Oliveira, Francesc Zarzuela, Alejandro Mediavilla, Patricia Martínez-Vallejo, Aroa Silgado, Lidia Goterris, Marc Muixí, Alberto Abelló, Anna Veiga, Daniel López-Codina, Elena Sulleiro (), Elisa Sayrol () and Joan Joseph-Munné ()
Additional contact information
Carles Rubio Maturana: Microbiology Department, Vall d’Hebron University Hospital, Vall d’Hebron Research Institute (VHIR), 08035 Barcelona, Spain
Allisson Dantas de Oliveira: Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), 08860 Castelldefels, Spain
Francesc Zarzuela: Microbiology Department, Vall d’Hebron University Hospital, Vall d’Hebron Research Institute (VHIR), 08035 Barcelona, Spain
Alejandro Mediavilla: Microbiology Department, Vall d’Hebron University Hospital, Vall d’Hebron Research Institute (VHIR), 08035 Barcelona, Spain
Patricia Martínez-Vallejo: Microbiology Department, Vall d’Hebron University Hospital, Vall d’Hebron Research Institute (VHIR), 08035 Barcelona, Spain
Aroa Silgado: Microbiology Department, Vall d’Hebron University Hospital, Vall d’Hebron Research Institute (VHIR), 08035 Barcelona, Spain
Lidia Goterris: Microbiology Department, Vall d’Hebron University Hospital, Vall d’Hebron Research Institute (VHIR), 08035 Barcelona, Spain
Marc Muixí: Microbiology Department, Vall d’Hebron University Hospital, Vall d’Hebron Research Institute (VHIR), 08035 Barcelona, Spain
Alberto Abelló: Database Technologies and Information Management Group, Service and Information Systems Engineering Department, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain
Anna Veiga: Probitas Foundation, 08022 Barcelona, Spain
Daniel López-Codina: Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), 08860 Castelldefels, Spain
Elena Sulleiro: Microbiology Department, Vall d’Hebron University Hospital, Vall d’Hebron Research Institute (VHIR), 08035 Barcelona, Spain
Elisa Sayrol: Tecnocampus, Universitat Pompeu Fabra, 08302 Mataró, Spain
Joan Joseph-Munné: Microbiology Department, Vall d’Hebron University Hospital, Vall d’Hebron Research Institute (VHIR), 08035 Barcelona, Spain

IJERPH, 2024, vol. 22, issue 1, 1-11

Abstract: The gold standard diagnosis for malaria is the microscopic visualization of blood smears to identify Plasmodium parasites, although it is an expert-dependent technique and could trigger diagnostic errors. Artificial intelligence (AI) tools based on digital image analysis were postulated as a suitable supportive alternative for automated malaria diagnosis. A diagnostic evaluation of the iMAGING AI-based system was conducted in the reference laboratory of the International Health Unit Drassanes-Vall d’Hebron in Barcelona, Spain. iMAGING is an automated device for the diagnosis of malaria by using artificial intelligence image analysis tools and a robotized microscope. A total of 54 Giemsa-stained thick blood smear samples from travelers and migrants coming from endemic areas were employed and analyzed to determine the presence/absence of Plasmodium parasites. AI diagnostic results were compared with expert light microscopy gold standard method results. The AI system shows 81.25% sensitivity and 92.11% specificity when compared with the conventional light microscopy gold standard method. Overall, 48/54 (88.89%) samples were correctly identified [13/16 (81.25%) as positives and 35/38 (92.11%) as negatives]. The mean time of the AI system to determine a positive malaria diagnosis was 3 min and 48 s, with an average of 7.38 FoV analyzed per sample. Statistical analyses showed the Kappa Index = 0.721, demonstrating a satisfactory correlation between the gold standard diagnostic method and iMAGING results. The AI system demonstrated reliable results for malaria diagnosis in a reference laboratory in Barcelona. Validation in malaria-endemic regions will be the next step to evaluate its potential in resource-poor settings.

Keywords: artificial intelligence; malaria; automated diagnosis; tropical medicine; Plasmodium; point-of-care; infectious diseases (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/22/1/47/pdf (application/pdf)
https://www.mdpi.com/1660-4601/22/1/47/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:22:y:2024:i:1:p:47-:d:1557975

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().