Over 1 month, anonymous players from 95 countries played more than 12,000 games and generated a database of more than 270,000 clicks on the test images. In order to combine the choices of different players into a single crowd decision, we implemented an image processing pipeline and a quorum algorithm that judged a parasite tagged when a group of players agreed on its position. In the event that players found all the parasites present in the image, they were presented with a new image. In the game, players had to find and tag as many parasites as possible in 1 minute. Random images of thick blood films containing Plasmodium falciparum at medium to low parasitemias, acquired by conventional optical microscopy, were presented to players. Data were collected through the MalariaSpot website. The experimental system consisted of a Web-based game where online volunteers were tasked with detecting parasites in digitized blood sample images coupled with a decision algorithm that combined the analyses from several players to produce an improved collective detection outcome. In particular, we investigated whether anonymous volunteers with no prior experience would be able to count malaria parasites in digitized images of thick blood smears by playing a Web-based game. This research tests the feasibility of a crowdsourced approach to malaria image analysis. The gold standard for estimating the parasite burden and the corresponding severity of the disease consists in manually counting the number of parasites in blood smears through a microscope, a process that can take more than 20 minutes of an expert microscopist's time.
![angel parasite game angel parasite game](https://i1.rgstatic.net/publication/233804789_Crowdsourcing_Malaria_Parasite_Quantification_An_Online_Game_for_Analyzing_Images_of_Infected_Thick_Blood_Smears/links/5b9bb67b299bf13e602f893a/largepreview.png)
There are 600,000 new malaria cases daily worldwide.