Proposed platform.

<p><b>A</b>) Biomedical data (e.g., images of thin blood smear samples) from individual light microscopes all around the world are transmitted to data centres where they are pre-processed and digitally distributed among gamers, which in turn diagnose and transmit their responses back. These individual results of the gamers are then fused toward a final diagnosis, the result of which is transmitted back to the point-of-care or the clinic/hospital. In the map above, orange-coloured regions show locations where risk of contraction of malaria still exists. <b>B</b>) Block diagram of the presented platform.</p

Summary of experimental results for diagnosis of malaria infected red blood cells.

<p>Summary of experimental results for diagnosis of malaria infected red blood cells.</p

The Crowd Effect: gamer performance results for experiment #5.

<p>The plots show the worst case scenarios where the diagnoses from the worst performing players are used to generate an overall diagnosis for each RBC in the game. Note that the specificity (or true negative rate) is always very high for the gamers, and does not improve much as more gamers are added to the mix. However, the sensitivity (or true positive rate) benefits the most as more players are added, and climbs above 95% once 15-gamers form the crowd. The accuracy also increases as more players are added, but since it reflects both the specificity and the sensitivity its increase is not as drastic as that of the sensitivity.</p

Overview of the gaming analysis framework.

<p>The images are treated as a sequence of binary values that are broadcast by the server. The gamers are effectively noisy repeaters that in the most ideal case output the correct symbol for the inputs that they receive. Each repeater transmits its own noisy version of the same input symbol to a decoder. The decoder combines all the received repeater outputs and decodes a final output z_i, which ideally will be the correct label/diagnosis for the input images. The repeaters can be modelled as Binary Communication Channels (top-left). <i>p_ij</i> corresponds to the probability of receiving symbol <i>j</i> when in fact symbol <i>i</i> was transmitted.</p

The hybrid (human + machine) diagnostics framework.

<p>As new images are generated, they are diagnosed by pre-trained machine learning algorithms. The confidence of these algorithms in their decisions determines whether the images should be passed on to human gamers or not. Once the <i>difficult-to-diagnose</i> images for these algorithms are crowd-sourced and are diagnosed by the human gamers, they are merged with the <i>easy-to-diagnose</i> images to compute the final diagnostics results. The data is then fed back through the system and added to the training dataset used by the machine learning algorithms. During each cycle, self-learning algorithms will improve as a result of added training data. ‘T’ refers to a threshold value.</p