Collaborative intelligence and gamification for on-line malaria species differentiation

Postigo Mijarra, Jose Maria and Cuadrado Sánchez, Daniel and Ledesma Carbayo, Maria Jesus and Santos Lleo, Andres De and Luengo Oroz, Miguel Angel and Linares Liébana, María and Gil-casanova, Sara and Vladimirov, Alexander and Ortiz-ruiz, Alejandra and Garcia-villena, Jaime and Nunez-escobedo, Jose Maria and Martinez-lopez, Joaquin and Miguel Rubio, Jose and Bassat, Quique (2019). Collaborative intelligence and gamification for on-line malaria species differentiation. "MALARIA JOURNAL", v. 18 (n. 21); pp.. ISSN 1475-2875.


Title: Collaborative intelligence and gamification for on-line malaria species differentiation
  • Postigo Mijarra, Jose Maria
  • Cuadrado Sánchez, Daniel
  • Ledesma Carbayo, Maria Jesus
  • Santos Lleo, Andres De
  • Luengo Oroz, Miguel Angel
  • Linares Liébana, María
  • Gil-casanova, Sara
  • Vladimirov, Alexander
  • Ortiz-ruiz, Alejandra
  • Garcia-villena, Jaime
  • Nunez-escobedo, Jose Maria
  • Martinez-lopez, Joaquin
  • Miguel Rubio, Jose
  • Bassat, Quique
Item Type: Article
Título de Revista/Publicación: MALARIA JOURNAL
Date: 2019
ISSN: 1475-2875
Volume: 18
Freetext Keywords: Crowdsourcing; Malaria classification; Image analysis; Games for health; Telepathology
Faculty: E.T.S.I. Montes (UPM)
Department: Silvopascicultura [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

Funding Projects

Government of SpainTEC2015-66978-RUnspecifiedUnspecifiedTecnología óptica para elastografía del tejido
Government of SpainFPDI ‑2013‑16409UnspecifiedUnspecifiedUnspecified
Universidad Politécnica de MadridCOOP-XVII-02UnspecifiedM. JESÚS LEDESMA CARBAYOTecnologías de adquisición y procesamiento de imágenes para diagnóstico de enfermedades oculares, tuberculosis infantil, e infección por helmintos en paises en desarrollo
Madrid Regional GovernmentS2013/MIT-3024TOPUSUnspecifiedTomografía por emisión de positrones y ultrasonidos
Government of SpainSNEO-20171197NEOTECUnspecifiedUnspecified

More information

Item ID: 64264
DC Identifier:
OAI Identifier:
DOI: 10.1186/s12936-019-2662-9
Official URL:
Deposited by: Memoria Investigacion
Deposited on: 04 Nov 2020 09:32
Last Modified: 04 Nov 2020 09:32
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