Collaborative intelligence and gamification for on-line malaria species differentiation

• Published in: Malaria Journal
• Main Authors: María Linares, María Postigo, Daniel Cuadrado, Alejandra Ortiz-Ruiz, Sara Gil-Casanova, Alexander Vladimirov, Jaime García-Villena, José María Nuñez-Escobedo, Joaquín Martínez-López, José Miguel Rubio, María Jesús Ledesma-Carbayo, Andrés Santos, Quique Bassat, Miguel Luengo-Oroz
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2019
• Subjects: Crowdsourcing; Malaria classification; Image analysis; Games for health; Telepathology; Arctic medicine. Tropical medicine; RC955-962; Infectious and parasitic diseases; RC109-216; Arctic
• Online Access: https://doi.org/10.1186/s12936-019-2662-9; https://doaj.org/article/e8343c17ff1e4e1291bdc1ed727c1d87

Abstract 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. ...