Electrical Engineering Department, University of California Los Angeles, Los Angeles, California, United States of America.
PLoS One. 2012;7(5):e37245. doi: 10.1371/journal.pone.0037245. Epub 2012 May 11.
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.
在这项工作中，我们研究了未经训练的人类的先天视觉识别和学习能力是否可以用于对生物医学样本进行可靠的显微镜分析，以进行诊断。为此，我们设计了有趣的数字游戏，这些游戏与人工智能学习和处理后端接口，以证明在进行二元医疗诊断决策（例如，感染与未感染）的情况下，通过使用众包游戏，可以接近医学专家做出此类诊断的准确性。具体来说，我们使用非专业游戏玩家报告疟疾感染的红细胞的诊断准确性，其准确率与受过培训的医疗专业人员做出的诊断决策相差在 1.25%以内。
