Can large groups of public non-experts be trained to recognize infectious diseases with the accuracy of a trained pathologist? Findings recently published in PLoS ONE suggest the answer may be "yes."
In the study (.pdf), UC Los Angeles researchers developed an online gaming system in which users distinquish malaria-infected red blood cells from healthy ones by viewing digital images obtained from microscope. Prior to playing the game, users were required to complete a short tutorial describing the rules and explaining how to identify malaria-infected red blood cells using example images. Gamers then completed a training module and were required to achieve greater than 99 percent accuracy in order to continue playing the game. If they failed to meet this standard, gamers were asked to replay the practice game until achieving greater than 99 percent accuracy.
According to a university release:
The UCLA team found that a small group of non-experts playing the game (mostly undergraduate student volunteers) was collectively able to diagnosis malaria-infected red blood cells with an accuracy that was within 1.25 percent of the diagnostic decisions made by a trained medical professional.
The game, which can be accessed on cell phones and personal computers, can be played by anyone around the world, including children.
Crowdsourcing, the UCLA researchers say, could potentially help overcome limitations in the diagnosis of malaria, which affects some 210 million people annually worldwide and accounts for 20 percent of all childhood deaths in sub-Saharan Africa and almost 40 percent of all hospitalizations throughout that continent.
Typically, malaria diagnosis involves a time-consuming process during which a trained pathologist uses a conventional light microscope to view images of cells and count the number of malaria-causing parasites. Using a crowdsourcing method, such as the UCLA game, to train non-professionals to identify malaria could be an affordable option to speed up diagnosis.