RT Journal Article
T1 Collaborative intelligence and gamification for on-line malaria species differentiation
A1 Linares Gómez, María
A1 Postigo, María
A1 Cuadrado, Daniel
A1 Ortiz-Ruiz, Alejandra
A1 Gil-Casanova, Sara
A1 Vladimirov, Alexander
A1 García-Villena, Jaime
A1 Nuñez-Escobedo, José María
A1 Martínez López, Joaquín
A1 Rubio, José Miguel
A1 Ledesma-Carbayo, María Jesús
A1 Santos, Andrés
A1 Bassat, Quique
A1 Luengo-Oroz, Miguel
AB Background: Current World Health Organization recommendations for the management of malaria include the need for a parasitological confrmation 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 identifcation and diferentia‑ tion 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 diferentiate the young trophozoite stage of the fve 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. After2 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 diferentiation. Statistically, when the choice of several players was combined (n>25), they were able to signifcantly 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 fndings show that it is possible to train malaria-naïve non-experts to identify and diferentiate 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.
SN 1475-2875
YR 2019
FD 2019-01-24
LK https://hdl.handle.net/20.500.14352/101970
UL https://hdl.handle.net/20.500.14352/101970
LA eng
NO Spanish Ministry of Economy and Competitiveness
NO Spanish Society of Hematology and Hemotherapy
NO Universidad Politécnica de Madrid
NO Madrid Regional Government
NO Spain’s Science, Innovation & Universities Ministry
NO Spanish Ministry of Economy, Industry and Competitiveness
NO European Regional Development Funds
NO Amazon Web Services
NO Fundación Renta Corporación
NO Ashoka
DS Docta Complutense
RD 8 abr 2025