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dc.contributor.authorLinares, María
dc.contributor.authorPostigo, María
dc.contributor.authorCuadrado, Daniel
dc.contributor.authorOrtiz-Ruiz, Alejandra
dc.contributor.authorGil-Casanova, Sara
dc.contributor.authorVladimirov, Alexander
dc.contributor.authorGarcía-Villena, Jaime
dc.contributor.authorNuñez-Escobedo, José María
dc.contributor.authorMartinez-Lopez, Joaquin 
dc.contributor.authorRubio Muñoz, Jose Miguel 
dc.contributor.authorLedesma-Carbayo, María Jesús
dc.contributor.authorSantos, Andres
dc.contributor.authorBassat, Quique
dc.contributor.authorLuengo-Oroz, Miguel
dc.identifier.citationMalar J. 2019 Jan 24;18(1):21.es_ES
dc.description.abstractBACKGROUND: 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.es_ES
dc.description.sponsorshipM.L. held a postdoctoral Fellowship of the Spanish Ministry of Economy and Competitiveness (FPDI-2013-16409) and holds a grant from the Spanish Society of Hematology and Hemotherapy. This work was supported by the Universidad Politécnica de Madrid (COOP-XVII-02), Spain’s Science, Innovation & Universities Ministry (TEC2015-66978-R), Madrid Regional Government (TOPUS S2013/MIT-3024), the CDTI NEOTEC SNEO-20171197 grant from the Spanish Ministry of Economy, Industry and Competitiveness, the European Regional Development Funds, Amazon Web Services, Fundación Renta Corporación and Ashoka. ISGlobal is a member of the CERCA Programme, Generalitat de Catalunya. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.es_ES
dc.publisherBioMed Central (BMC) es_ES
dc.subjectGames for healthes_ES
dc.subjectImage analysises_ES
dc.subjectMalaria classificationes_ES
dc.subject.meshCrowdsourcing es_ES
dc.subject.meshMalaria es_ES
dc.subject.meshOnline Systems es_ES
dc.subject.meshPlasmodium es_ES
dc.subject.meshSpecies Specificity es_ES
dc.subject.meshTrophozoites es_ES
dc.subject.meshVideo Games es_ES
dc.titleCollaborative intelligence and gamification for on-line malaria species differentiationes_ES
dc.typeresearch articlees_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España) 
dc.contributor.funderTechnical University of Madrid (España) 
dc.contributor.funderComunidad de Madrid (España) 
dc.contributor.funderMinisterio de Economía, Industria y Competitividad (España) 
dc.contributor.funderUnión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF) 
dc.contributor.funderAmazon Web Services 
dc.contributor.funderFundación Renta Corporación 
dc.contributor.funderFundación Ashoka 
dc.identifier.journalMalaria journales_ES
dc.repisalud.centroISCIII::Centro Nacional de Microbiologíaes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/NEOTEC SNEO-20171197es_ES
dc.rights.accessRightsopen accesses_ES

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