PURPOSE OF REVIEW: Crowdsourcing involves the use of the collective intelligence of online communities to produce solutions and outcomes for defined objectives. The use of crowdsourcing is growing in many scientific areas. Crowdsourcing in ophthalmology has been used in basic science and clinical research; however, it also shows promise as a method with wide-ranging applications. This review presents current findings on the use of crowdsourcing in ophthalmology and potential applications in the future. RECENT FINDINGS: Crowdsourcing has been used to distinguish normal retinal images from images with diabetic retinopathy; the collective intelligence of the crowd was able to correctly classify 81% of 230 images (19 unique) for US$1.10/eye in 20?min. Crowdsourcing has also been used to distinguish normal optic discs from abnormal ones with reasonable sensitivity (83–88%), but low specificity (35–43%). Another study used crowdsourcing for quick and reliable manual segmentation of optical coherence tomography images. Outside of ophthalmology, crowdsourcing has been used for text and image interpretation, language translation, and data analysis. SUMMARY: Crowdsourcing has the potential for rapid and economical data processing. Among other applications, it could be used in research settings to provide the ‘ground-truth’ data, and in the clinical settings to relieve the burden of image processing on experts.
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