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Distributed Medical Image Analysis and Diagnosis through Crowd-Sourced Games: A Malaria Case Study

Author

Listed:
  • Sam Mavandadi
  • Stoyan Dimitrov
  • Steve Feng
  • Frank Yu
  • Uzair Sikora
  • Oguzhan Yaglidere
  • Swati Padmanabhan
  • Karin Nielsen
  • Aydogan Ozcan

Abstract

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.

Suggested Citation

  • Sam Mavandadi & Stoyan Dimitrov & Steve Feng & Frank Yu & Uzair Sikora & Oguzhan Yaglidere & Swati Padmanabhan & Karin Nielsen & Aydogan Ozcan, 2012. "Distributed Medical Image Analysis and Diagnosis through Crowd-Sourced Games: A Malaria Case Study," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-8, May.
    • Handle: RePEc:plo:pone00:0037245
    DOI: 10.1371/journal.pone.0037245
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    References listed on IDEAS

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    1. Seth Cooper & Firas Khatib & Adrien Treuille & Janos Barbero & Jeehyung Lee & Michael Beenen & Andrew Leaver-Fay & David Baker & Zoran Popović & Foldit players, 2010. "Predicting protein structures with a multiplayer online game," Nature, Nature, vol. 466(7307), pages 756-760, August.
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    Cited by:

    1. Konstantinos Mitsakakis & Sebastian Hin & Pie Müller & Nadja Wipf & Edward Thomsen & Michael Coleman & Roland Zengerle & John Vontas & Konstantinos Mavridis, 2018. "Converging Human and Malaria Vector Diagnostics with Data Management towards an Integrated Holistic One Health Approach," IJERPH, MDPI, vol. 15(2), pages 1-26, February.
    2. Li, Xiaoou & Chen, Yunxiao & Chen, Xi & Liu, Jingchen & Ying, Zhiliang, 2021. "Optimal stopping and worker selection in crowdsourcing: an adaptive sequential probability ratio test framework," LSE Research Online Documents on Economics 100873, London School of Economics and Political Science, LSE Library.
    3. Barbara Strobl & Simon Etter & Ilja van Meerveld & Jan Seibert, 2019. "The CrowdWater game: A playful way to improve the accuracy of crowdsourced water level class data," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-23, September.

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