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Collaborative intelligence and gamification for on-line malaria species differentiation.

María LinaresMaría PostigoDaniel CuadradoAlejandra Ortiz-RuizSara Gil-CasanovaAlexander VladimirovJaime García-VillenaJosé María Nuñez-EscobedoJoaquín Martínez-LópezJosé Miguel RubioMaría Jesús Ledesma-CarbayoAndrés SantosQuique BassatMiguel Luengo-Oroz
Published in: Malaria journal (2019)
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.
Keyphrases
  • plasmodium falciparum
  • genetic diversity
  • high speed
  • resistance training
  • mass spectrometry
  • high intensity