To address the global bottleneck in the field of medical image diagnosis, Miguel Luengo-Oroz has developed a tool that harnesses the power of people to expedite the process of diagnosis and reduce its costs. Through innovative online gaming methodology, this crowdsourced model enables people to help identify health symptoms in their global neighbors in distant parts of the world. MalariaSpot is an online game that directly supports malaria diagnosis based on three pillars. The first is the contributions of thousands of citizens connected through the Internet. Certain specific image analysis tasks—such as recognizing malaria parasites—can be quickly learned by non-specialists, therefore exponentially increasing the potential global “workforce” devoted to image diagnosis, while saving the valuable time of medical specialists. Second, Miguel has designed his platform to take advantage of users’ abilities to interact and play in digital worlds, a space which often poses more of a barrier than familiarity with biomedical images. Miguel has also imbued his platform with a competitive edge, incentivizing and motivating players to make accurate diagnoses through gamification of the crowdsourcing approach.
The Malariaspot game is a kick-off proof-of-concept experiment, which is part of a larger vision devoted to: The establishment of a global specialized task force of remote gamers/workers able to perform on-line malaria diagnosis (and potentially other diseases). The development of new on-line games powered by artificial intelligence engines able to diagnose and minimize the time required get a perfect parasite count. Developing a microscopy-in-a-mobile-phone system for telediagnosis, allowing data transfer directly from field workers and health centers to the Malariaspot platform for rapid diagnosis.