2026-06-14T03:33:06Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/74422024-11-28T15:54:41Zcom_20.500.12105_2052com_20.500.12105_2051com_20.500.12105_2173com_20.500.12105_15322col_20.500.12105_19609col_20.500.12105_19597col_20.500.12105_16938
Repisalud author Linares, María author Postigo, María author Cuadrado, Daniel author Ortiz-Ruiz, Alejandra author Gil-Casanova, Sara author Vladimirov, Alexander author García-Villena, Jaime author Nuñez-Escobedo, José María author Martinez-Lopez, Joaquin author Rubio Muñoz, Jose Miguel author Ledesma-Carbayo, María Jesús author Santos, Andres author Bassat, Quique author Luengo-Oroz, Miguel funder Ministerio de Ciencia, Innovación y Universidades (España) funder Technical University of Madrid (España) funder Comunidad de Madrid (España) funder Ministerio de Economía, Industria y Competitividad (España) funder Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF) funder Amazon Web Services funder Fundación Renta Corporación funder Fundación Ashoka 2019-04-10T11:03:35Z 2019-04-10T11:03:35Z 2019-01-24 Malar J. 2019 Jan 24;18(1):21. 10.1186/s12936-019-2662-9 1475-2875 Malaria journal 30678733 http://hdl.handle.net/20.500.12105/7442 BACKGROUND: Current World Health Organization recommendations for the management of malaria include the need for a parasitological confirmation prior to triggering appropriate treatment. The use of rapid diagnostic tests (RDTs) for malaria has contributed to a better infection recognition and a more targeted treatment. Nevertheless, low-density infections and parasites that fail to produce HRP2 can cause false-negative RDT results. Microscopy has traditionally been the methodology most commonly used to quantify malaria and characterize the infecting species, but the wider use of this technique remains challenging, as it requires trained personnel and processing capacity. OBJECTIVE: In this study, the feasibility of an on-line system for remote malaria species identification and differentiation has been investigated by crowdsourcing the analysis of digitalized infected thin blood smears by non-expert observers using a mobile app. METHODS: An on-line videogame in which players learned how to differentiate the young trophozoite stage of the five Plasmodium species has been designed. Images were digitalized with a smartphone camera adapted to the ocular of a conventional light microscope. Images from infected red blood cells were cropped and puzzled into an on-line game. During the game, players had to decide the malaria species (Plasmodium falciparum, Plasmodium malariae, Plasmodium vivax, Plasmodium ovale, Plasmodium knowlesi) of the infected cells that were shown in the screen. After 2 months, each player's decisions were analysed individually and collectively. RESULTS: On-line volunteers playing the game made more than 500,000 assessments for species differentiation. Statistically, when the choice of several players was combined (n > 25), they were able to significantly discriminate Plasmodium species, reaching a level of accuracy of 99% for all species combinations, except for P. knowlesi (80%). Non-expert decisions on which Plasmodium species was shown in the screen were made in less than 3 s. CONCLUSION: These findings show that it is possible to train malaria-naïve non-experts to identify and differentiate malaria species in digitalized thin blood samples. Although the accuracy of a single player is not perfect, the combination of the responses of multiple casual gamers can achieve an accuracy that is within the range of the diagnostic accuracy made by a trained microscopist. eng Crowdsourcing Games for health Image analysis Malaria classification Telepathology Collaborative intelligence and gamification for on-line malaria species differentiation research article 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 URL https://repisalud.isciii.es/bitstreams/aaf86590-ce49-4e65-9784-7d1cdd71dd91/download File MD5 47fdd6c09a3fbe58a2dab9948ef8c55a 1813673 application/pdf CollaborativeIntelligenceAndGamification_2019.pdf URL https://repisalud.isciii.es/bitstreams/55ed3d3b-5994-4835-8851-14f40aa1f1c4/download File MD5 4db28a02255fef1e28a552ddccd3fe92 40312 text/plain CollaborativeIntelligenceAndGamification_2019.pdf.txt