We have recently proposed a mathematical platform for crowd-sourcing of biomedical

We have recently proposed a mathematical platform for crowd-sourcing of biomedical image analysis and analysis through digital gaming. efforts over the past few years is definitely that of computational microscopy.1C7 Another dimensions of medical imaging’s evolution has been a consequence of rapid advances in telecommunications and the coming of age of the Internet. These days an X-ray or perhaps a microscope slide image can be viewed almost instantaneously thousands of miles away from the point of capture by an expert who experienced no involvement in the imaging process. This unprecedented level of access to medical images and Rabbit Polyclonal to MASTL data is now opening up fresh approaches to medical analysis, heralding the age of telemedicine, where one can outsource medical analysis to doctors in faraway locations, while making it significantly better to get a second opinion on a particular analysis. This brings an interesting question to mind: What if instead of getting a second opinion, one could quickly get tens or hundreds of opinions all at once? Would it become possible to combine 249921-19-5 IC50 responses from many individuals to arrive at an accurate analysis decision? Put in a different way: Could we crowd-source medical analysis? One of the 1st records on the use of the knowledge of the crowd goes back to the 19th century statistician Sir Francis Galton, who in 1907 reported on a peculiar contest that he experienced at a livestock fair.8 For a small cost, the participants entered a contest to think the weight of an ox on display, with those coming closest to the true weight winning prizes. Having about 800 contestants, nobody guessed the exact weight. However, Galton observed the median of the weights guessed by all the 249921-19-5 IC50 participants was only 9?lbs more than the true excess weight of 1 1,198?lbsor just off 249921-19-5 IC50 by 0.8 percent! Over the past decade, there have been several projects that have crowd-sourced hard pattern analysis and acknowledgement jobs to individuals around the world. Perhaps one of the most successful of these is definitely reCAPTCHA9a crowd-sourcing project for 249921-19-5 IC50 digitizing books along with other nondigital images. FoldIt10,11 and EteRNA12 are two additional projects that have crowd-sourced the task of scientific finding to ordinary individuals through entertaining games. They all make use of the superior pattern-recognition capabilities of humans to solve tasks that would be hard and time-consuming to solve by computers. We have recently taken a similar approach to test the idea of crowd-sourcing medical analysis and in the beginning tackled the problem of identifying malaria-infected blood cells, a task that normally demands professional teaching.13,14 Malaria 249921-19-5 IC50 is a major health problem in many tropical and subtropical climates, including much of sub-Saharan Africa. It is a disease that affects a rather large number of people every year. According to the World Health Organization’s estimate, there were 174 million instances of malaria in 2010 2010 that resulted in 655,000 deaths, where >90% of these deaths occurred in Africa.15 For analysis of malaria, conventional light microscopy remains as one of the platinum standard methods, with 165 million instances having been diagnosed through this method in 2010 2010.15 A pathologist must typically check on the order of 1,000 individual red blood cells under a high-magnification light microscope before being able to reliably call a sample healthy or negative. This, regrettably, is a time-consuming and demanding task given the large number of instances observed, resulting in a false-positive rate of, for example, approximately 60 percent in some developing countries. 16 Such a high false-positive rate can lead to unneeded treatments and hospitalizations. Materials and Methods To test our idea, we started by creating interesting digital games (termed BioGames) (Fig. 1) where the players were presented with a set of reddish blood cell images taken from potentially infected samples.13,14 They were allowed to choose to digitally get rid of or standard bank the infected and healthy cells, respectively. To be able to later on combine the information generated by multiple gamers, we had to know how they were doing.

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