02378nas a2200253   4500000000100000008004100001260003200042100001000074700002100084700002200105700001500127700001600142700001300158700001100171700001100182700001400193700002200207700001800229245013500247300001400382490000900396520170500405022001402110       2021                            d  c11/2021bScientific Reports1 aLin L1 aBermejo-Pelaez D1 aCapellan-Martin D1 aCuadrado D1 aRodriguez C1 aGarcia L1 aDiez N1 aTome R1 aPostigo M1 aLedesma-Carbayo M1 aLuengo-Oroz M00aCombining collective and artificial intelligence for global health diseases diagnosis using crowdsourced annotated medical images.  a3344-33480 v20213 a<p>Visual inspection of microscopic samples is still the gold standard diagnostic methodology for many global health diseases. Soil-transmitted helminth infection affects 1.5 billion people worldwide, and is the most prevalent disease among the Neglected Tropical Diseases. It is diagnosed by manual examination of stool samples by microscopy, which is a time-consuming task and requires trained personnel and high specialization. Artificial intelligence could automate this task making the diagnosis more accessible. Still, it needs a large amount of annotated training data coming from experts.In this work, we proposed the use of crowdsourced annotated medical images to train AI models (neural networks) for the detection of soil-transmitted helminthiasis in microscopy images from stool samples leveraging non-expert knowledge collected through playing a video game. We collected annotations made by both school-age children and adults, and we showed that, although the quality of crowdsourced annotations made by school-age children are sightly inferior than the ones made by adults, AI models trained on these crowdsourced annotations perform similarly (AUC of 0.928 and 0.939 respectively), and reach similar performance to the AI model trained on expert annotations (AUC of 0.932). We also showed the impact of the training sample size and continuous training on the performance of the AI models.In conclusion, the workflow proposed in this work combined collective and artificial intelligence for detecting soil-transmitted helminthiasis. Embedded within a digital health platform can be applied to any other medical image analysis task and contribute to reduce the burden of disease.</p>
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