2024-03-29T04:50:18Zhttp://repisalud.isciii.es/oai/requestoai:repisalud.isciii.es:20.500.12105/74422022-11-22T14:21:26Zcom_20.500.12105_2174com_20.500.12105_2051com_20.500.12105_2173com_20.500.12105_2060com_20.500.12105_2052col_20.500.12105_2175col_20.500.12105_2061
00925njm 22002777a 4500
dc
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
author
2019-01-24
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.
Malar J. 2019 Jan 24;18(1):21.
1475-2875
http://hdl.handle.net/20.500.12105/7442
30678733
10.1186/s12936-019-2662-9
Malaria journal
Crowdsourcing
Games for health
Image analysis
Malaria classification
Telepathology
Collaborative intelligence and gamification for on-line malaria species differentiation