Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria
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 suitabl...
| Authors: | ; ; ; ; ; ; ; ; ; ; ; ; ; |
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| Format: | Article |
| Publication date: | 2025 |
| Country: | España |
| Institution: | Universitat Autònoma de Barcelona |
| Repository: | Dipòsit Digital de Documents de la UAB |
| Language: | English |
| OAI Identifier: | oai:ddd.uab.cat:307843 |
| Online Access: | https://ddd.uab.cat/record/307843 https://dx.doi.org/urn:doi:10.3390/ijerph22010047 |
| Access Level: | Open access |
| Keyword: | Artificial intelligence Malaria Automated diagnosis Tropical medicine Plasmodium Point-of-care Infectious diseases |
| _version_ | 1878732801458569216 |
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| author | Rubio Maturana, Carles |
| author2 | Dantas de Oliveira, Allisson|||0000-0002-8267-9760 Serrat, Francesc Zarzuela|||0000-0001-7520-978X Mediavilla, Alejandro Martínez-Vallejo, Patricia Silgado, Aroa|||0000-0001-7581-0049 Goterris, Lidia|||0000-0003-1471-4461 Muixí, Marc Abelló, Alberto|||0000-0002-3223-2186 Veiga, Anna|||0000-0002-0943-9904 López i Codina, Daniel|||0000-0002-0408-4526 Sulleiro, Elena|||0000-0002-9783-6060 Sayrol, Elisa|||0000-0002-0526-9733 Joseph-Munné, Joan |
| author2_role | author author author author author author author author author author author author author |
| author_browse | Abelló, Alberto|||0000-0002-3223-2186 Dantas de Oliveira, Allisson|||0000-0002-8267-9760 Goterris, Lidia|||0000-0003-1471-4461 Joseph-Munné, Joan López i Codina, Daniel|||0000-0002-0408-4526 Martínez-Vallejo, Patricia Mediavilla, Alejandro Muixí, Marc Rubio Maturana, Carles Sayrol, Elisa|||0000-0002-0526-9733 Serrat, Francesc Zarzuela|||0000-0001-7520-978X Silgado, Aroa|||0000-0001-7581-0049 Sulleiro, Elena|||0000-0002-9783-6060 Veiga, Anna|||0000-0002-0943-9904 |
| author_facet | Rubio Maturana, Carles Dantas de Oliveira, Allisson|||0000-0002-8267-9760 Serrat, Francesc Zarzuela|||0000-0001-7520-978X Mediavilla, Alejandro Martínez-Vallejo, Patricia Silgado, Aroa|||0000-0001-7581-0049 Goterris, Lidia|||0000-0003-1471-4461 Muixí, Marc Abelló, Alberto|||0000-0002-3223-2186 Veiga, Anna|||0000-0002-0943-9904 López i Codina, Daniel|||0000-0002-0408-4526 Sulleiro, Elena|||0000-0002-9783-6060 Sayrol, Elisa|||0000-0002-0526-9733 Joseph-Munné, Joan |
| author_role | author |
| collection | Dipòsit Digital de Documents de la UAB |
| description | 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_4a14cdd4e5e246c8d76f1f33b0eea404 |
| institution | Universitat Autònoma de Barcelona |
| instname_str | Universitat Autònoma de Barcelona |
| language | eng |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:ddd.uab.cat:307843 |
| publishDate | 2025 |
| publishDateSort | 2025 |
| reponame_str | Dipòsit Digital de Documents de la UAB |
| spelling | Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria Rubio Maturana, Carles Dantas de Oliveira, Allisson|||0000-0002-8267-9760 Serrat, Francesc Zarzuela|||0000-0001-7520-978X Mediavilla, Alejandro Martínez-Vallejo, Patricia Silgado, Aroa|||0000-0001-7581-0049 Goterris, Lidia|||0000-0003-1471-4461 Muixí, Marc Abelló, Alberto|||0000-0002-3223-2186 Veiga, Anna|||0000-0002-0943-9904 López i Codina, Daniel|||0000-0002-0408-4526 Sulleiro, Elena|||0000-0002-9783-6060 Sayrol, Elisa|||0000-0002-0526-9733 Joseph-Munné, Joan Artificial intelligence Malaria Automated diagnosis Tropical medicine Plasmodium Point-of-care Infectious diseases 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. https://ddd.uab.cat/record/307843 https://dx.doi.org/urn:doi:10.3390/ijerph22010047 |
| spellingShingle | Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria Rubio Maturana, Carles Artificial intelligence Malaria Automated diagnosis Tropical medicine Plasmodium Point-of-care Infectious diseases |
| title | Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria |
| title_full | Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria |
| title_fullStr | Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria |
| title_full_unstemmed | Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria |
| title_short | Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria |
| title_sort | Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria |
| topic | Artificial intelligence Malaria Automated diagnosis Tropical medicine Plasmodium Point-of-care Infectious diseases |
| topic_facet | Artificial intelligence Malaria Automated diagnosis Tropical medicine Plasmodium Point-of-care Infectious diseases |
| url | https://ddd.uab.cat/record/307843 https://dx.doi.org/urn:doi:10.3390/ijerph22010047 |