Distributed Medical Image Analysis and Diagnosis through Crowd-Sourced Games: A Malaria Case Study

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional

Spectral demultiplexing in holographic and fluorescent on-chip microscopy.

Lensfree on-chip imaging and sensing platforms provide compact and cost-effective designs for various telemedicine and lab-on-a-chip applications. In this work, we demonstrate computational solutions for some of the challenges associated with (i) the use of broadband, partially-coherent illumination sources for on-chip holographic imaging, and (ii) multicolor detection for lensfree fluorescent on-chip microscopy. Specifically, we introduce spectral demultiplexing approaches that aim to digitally narrow the spectral content of broadband illumination sources (such as wide-band light emitting diodes or even sunlight) to improve spatial resolution in holographic on-chip microscopy. We also demonstrate the application of such spectral demultiplexing approaches for wide-field imaging of multicolor fluorescent objects on a chip. These computational approaches can be used to replace e.g., thin-film interference filters, gratings or other optical components used for spectral multiplexing/demultiplexing, which can form a desirable solution for cost-effective and compact wide-field microscopy and sensing needs on a chip

Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study.

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional

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

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

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