Analysis and related diagnosis of medical images, regardless of the source and imaging modality, are tasks that require a great deal of expertise, demanding significant training of medical practitioners prior to being able to accurately interpret and diagnose such images. This is particularly true in analysis of microscopic data, creating challenges in resource-limited settings and developing countries, where properly trained health-care professionals are difficult to find.
We have shown that by utilizing the innate visual recognition and learning capabilities of human crowds it is possible to conduct reliable microscopic analysis of biomedical samples and make diagnostics decisions based on crowd-sourcing of microscopic data through intelligently designed and entertaining games that are interfaced with artificial learning and processing back-ends. We demonstrated that in the case of binary diagnostics decisions (e.g., infected vs. uninfected), using crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses.
Specifically, using non-professional gamers we report diagnosis of malaria infected red-blood-cells with an accuracy that is within 1.25% of the diagnostic decisions made by a trained professional.
Manuscript in PLoS ONE: PDF
Manuscript in Lab on a Chip: PDF
Manuscript in Games For Health: PDF
Manuscript in PLoS ONE: PDF
Number of Diagnoses per Player: Red: 0-100 || Blue: 101-1000 || Green: >1000
Together with crowd sourcing and social networks, today’s internet can be considered as a highly sophisticated “living and learning” system, where we have more than 100 trillion email messages every year, together with >300 million unique websites, with highly dynamic content. Our vision is to use this extra-ordinary medium to create a smart biomedical micro-analysis and diagnosis platform, leading the way to micro-world’s internet. For decades optical microscopy has been the workhorse of various fields including engineering, physical sciences, medicine and biology. Over the last few years, however, there has been a massive effort to create cost-effective, compact and lightweight microscope designs such that even mobile phones could be converted into microscopic analysis tools. The figure below shows examples of such compact microscopes that we have designed and built. Similar to the development of PC, this is a very important direction since it could enable wide-spread use of optical microscopy globally, with several orders of magnitude increase in the number of microscope users over the next few years. We aim to create a self-learning integrated network of microscopes toward smart biomedical micro-analysis and diagnosis. Once successful, this smart network of microscopes would deliver a paradigm-shift for medical, environmental, and biological sciences, among others, through various innovative uses of this network and its constantly expanding database.
According to the WHO, each year ~0.5 billion people become infected with malaria, and around 3 million die due to malaria infection. This world-map shows the regions that face the greatest risks in orange color. We aim to develop a self-learning distributed image analysis and diagnostics framework that is based on crowd-sourced games.