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.
The new UCLA study, "Distributed Medical Image Analysis and Diagnosis Through Crowd-Sourced Games," has been accepted for publication in the journal PLoS ONE. More information is available at http://biogames.ee.ucla.edu.
In addition to developing the crowd-sourced gaming platform that allows players to assist in identifying malaria in cells imaged under a light microscope, Ozcan's research group created an automated algorithm for diagnosing the same images using computer vision, as well as a novel hybrid platform for
|Contact: Wileen Wong Kromhout|
University of California - Los Angeles