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Open Access Publications from the University of California

Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study.

  • Author(s): Mavandadi, Sam
  • Dimitrov, Stoyan
  • Feng, Steve
  • Yu, Frank
  • Sikora, Uzair
  • Yaglidere, Oguzhan
  • Padmanabhan, Swati
  • Nielsen, Karin
  • Ozcan, Aydogan
  • et al.

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional.

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