Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria
Rubio Maturana, Carles (Universitat Autònoma de Barcelona. Departament de Genètica i de Microbiologia)
Dantas de Oliveira, Allisson (Universitat Politècnica de Catalunya. Departament de Física)
Serrat, Francesc Zarzuela (Hospital Universitari Vall d'Hebron)
Mediavilla, Alejandro (Universitat Autònoma de Barcelona. Departament de Genètica i de Microbiologia)
Martínez-Vallejo, Patricia (Universitat Autònoma de Barcelona. Departament de Genètica i de Microbiologia)
Silgado, Aroa (Hospital Universitari Vall d'Hebron)
Goterris, Lidia (Hospital Universitari Vall d'Hebron)
Muixí, Marc (Hospital Universitari Vall d'Hebron)
Abelló, Alberto (Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació)
Veiga, Anna (Probitas Foundation)
López i Codina, Daniel (Universitat Politècnica de Catalunya. Departament de Física)
Sulleiro Igual, Elena (Hospital Universitari Vall d'Hebron)
Sayrol, Elisa (Universitat Pompeu Fabra. Escola Superior Politècnica TecnoCampus)
Joseph-Munné, Joan (Hospital Universitari Vall d'Hebron)

Data:	2025
Resum:	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.
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 ; Malaria ; Automated diagnosis ; Tropical medicine ; Plasmodium ; Point-of-care ; Infectious diseases
Publicat a:	International journal of environmental research and public health, Vol. 22, Issue 1 (January 2025) , art. 47, ISSN 1660-4601

DOI: 10.3390/ijerph22010047
PMID: 39857500

11 p, 1.1 MB

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