Sistema de Salut de Catalunya
Institut Català de la Salut
[Rubio Maturana C, Mediavilla A, Martínez-Vallejo P, Goterris L] Servei de Microbiologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Department of Microbiology and Genetics, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain. [Dantas de Oliveira A] Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), Castelldefels, Spain. [Zarzuela F, Muixí M, Joseph-Munné J] Servei de Microbiologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. [Silgado A, Sulleiro E] Servei de Microbiologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Department of Microbiology and Genetics, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain. Centro de Investigación Biomédica en Red Enfermedades
Vall d'Hebron Barcelona Hospital Campus
2025-03-14T13:29:33Z
2025-03-14T13:29:33Z
2024
2025
Artificial intelligence; Automated diagnosis; Malaria
Inteligencia artificial; Diagnóstico automatizado; Malaria
Intel·ligència artificial; Diagnòstic automatitzat; Malària
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.
This research was co-funded by the Microbiology Department of Vall d’Hebron University Hospital and the Cooperation Centre of the Universitat Politècnica de Catalunya (CCD-UPC) and Probitas Foundation.
Article
Published version
English
Intel·ligència artificial - Aplicacions a la medicina; Microscòpia clínica; Malària - Diagnòstic; Robòtica en medicina; PHENOMENA AND PROCESSES::Mathematical Concepts::Algorithms::Artificial Intelligence; ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Microscopy; DISEASES::Parasitic Diseases::Protozoan Infections::Malaria; Other subheadings::Other subheadings::/diagnosis; INFORMATION SCIENCE::Information Science::Computing Methodologies::Algorithms::Artificial Intelligence::Robotics; FENÓMENOS Y PROCESOS::conceptos matemáticos::algoritmos::inteligencia artificial; TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::microscopía; ENFERMEDADES::enfermedades parasitarias::infecciones por protozoos::malaria; Otros calificadores::Otros calificadores::/diagnóstico; CIENCIA DE LA INFORMACIÓN::Ciencias de la información::metodologías computacionales::algoritmos::inteligencia artificial::robótica
MDPI
International Journal of Environmental Research and Public Health;22(1)
https://doi.org/10.3390/ijerph22010047
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
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