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Crowdsourcing: an overview and applications to ophthalmology
Xueyang Wang1, Lucy Mudie, Christopher J Brady
1Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Current Opinion in Ophthalmology
|January 14, 2016
Summary
Crowdsourcing leverages online communities for rapid and cost-effective data analysis in ophthalmology. This approach shows promise for tasks like image classification and segmentation, aiding both research and clinical applications.
Area of Science:
- Ophthalmology
- Computational Biology
- Medical Informatics
Background:
- Crowdsourcing utilizes collective intelligence from online communities for defined tasks.
- Its application is expanding across various scientific disciplines, including ophthalmology.
- This review explores current and future uses of crowdsourcing in ophthalmic research and practice.
Purpose of the Study:
- To review current findings on crowdsourcing applications in ophthalmology.
- To identify potential future applications of crowdsourcing in the field.
- To highlight the benefits of crowdsourcing for data processing and analysis in ophthalmology.
Main Methods:
- Literature review of studies employing crowdsourcing in ophthalmology.
- Analysis of crowdsourcing applications in image analysis, including classification and segmentation.
- Examination of cost-effectiveness and speed of crowdsourcing for data processing.
Main Results:
- Crowdsourcing accurately classified retinal images for diabetic retinopathy (81% accuracy).
- It demonstrated reasonable sensitivity (83-88%) but low specificity (35-43%) in distinguishing optic disc abnormalities.
- Crowdsourcing enabled quick and reliable manual segmentation of optical coherence tomography images.
Conclusions:
- Crowdsourcing offers a rapid and economical method for data processing in ophthalmology.
- It can provide essential 'ground-truth' data for research.
- Crowdsourcing has the potential to alleviate the workload of expert image analysis in clinical settings.
