Online crowd-sourcing — in which a task is presented to the public, who respond, for free, with various solutions and suggestions — has been used to evaluate potential consumer products, develop software algorithms and solve vexing research-and-development challenges. But diagnosing infectious diseases?
Working on the assumption that large groups of public non-experts can be trained to recognize infectious diseases with the accuracy of trained pathologists, researchers from the UCLA Henry Samueli School of Engineering and Applied Science and the David Geffen School of Medicine at UCLA have created a crowd-sourced online gaming system in which players distinguish malaria-infected red blood cells from healthy ones by viewing digital images obtained from microscopes. View the game.
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.
“The idea is, if you carefully combine the decisions of people — even non-experts — they become very competitive,” said Aydogan Ozcan, an associate professor of electrical engineering and bioengineering and the corresponding author of the crowd-sourcing research. “Also, if you just look at one person’s response, it may be OK, but that one person will inevitably make some mistakes. But if you combine 10 to 20, maybe 50 non-expert gamers together, you improve your accuracy greatly in terms of analysis.”
Crowd-sourcing, 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.
The current gold standard for malaria diagnosis involves a trained pathologist using a conventional light microscope to view images of cells and count the number of malaria-causing parasites. The process is very time-consuming, and given the large number of cases in resource-poor countries, the sheer volume presents a big challenge. In addition, a significant portion of cases reported in sub-Sahara Africa are actually false positives, leading to unnecessary and costly treatments and hospitalizations.
By training hundreds, and perhaps thousands, of members of the public to identify malaria through UCLA’s crowd-sourced game, a much greater number of diagnoses could be made more quickly — at no cost and with a high degree of collective accuracy.
“The idea is to use crowds to get collectively better in pathologic analysis of microscopic images, which could be applicable to various telemedicine problems,” said Sam Mavandadi, a postdoctoral scholar in Ozcan’s research group and the study’s first author.
Ozcan and Mavandadi emphasized that the same platform could be applied to combine the decisions of minimally trained health care workers to significantly boost the accuracy of diagnosis, which is especially promising for telepathology, among other telemedicine fields.
Play the game!