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

Sam Mavandadi,Stoyan Dimitrov,Oguzhan Yaglidere,Aydogan Ozcan,Steve Feng,Frank Yu,Karin Nielsen,Swati Padmanabhan,Uzair Sikora,Luzia Helena Carvalho

doi:10.1371/journal.pone.0037245

Sam Mavandadi, Stoyan Dimitrov + Show 8 more

Open Access

https://doi.org/10.1371/journal.pone.0037245

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Abstract

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.

Highlights

Crowd-sourcing is an emerging concept that has attracted significant attention in recent years as a strategy for solving computationally expensive and difficult problems [1,2,3,4,5,6]
Before starting to play the game, each gamer was given a brief online tutorial explaining the rules of the game and how malaria infected red blood cells (RBCs) typically look with some example images
Given that our focus was not to educate the players, and it was to demonstrate the quality of diagnostic results that can be achieved through untrained individuals, this initial test/ training game was designed in a simple repetitive fashion

Summary

Introduction

Crowd-sourcing is an emerging concept that has attracted significant attention in recent years as a strategy for solving computationally expensive and difficult problems [1,2,3,4,5,6] In this computing paradigm, pieces of difficult computational problems are distributed to a large number of individuals. Each participant completes one piece of the computational puzzle, sending the results back to a central system where they are all combined together to formulate the overall solution to the original problem In this context, crowd-sourcing is often used as a solution to various pattern-recognition and analysis tasks that may take computers long times to solve. EteRNA [9] is another game, which likewise makes use of crowds to get a better understanding of RNA folding

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