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A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability.

Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK. Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; Ophthalmology Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Moorfields Eye Hospital NHS Foundation Trust, London, UK; Health Data Research UK, London, UK; Centre for Regulatory Science and Innovation, Birmingham Health Partners, Birmingham, UK. Department of Ophthalmology and Visual Sciences, McGill University, Montreal, QC, Canada. Moorfields Eye Hospital NHS Foundation Trust, London, UK; Stanford University Byers Eye Institute, Palo Alto, CA, USA. Moorfields Eye Hospital NHS Foundation Trust, London, UK; Eye Clinic, Cantonal Hospital of Lucerne, Lucerne, Switzerland. National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK. Health Data Research UK, London, UK. International Centre for Eye Health, Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, UK. Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; Ophthalmology Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Health Data Research UK, London, UK; Centre for Regulatory Science and Innovation, Birmingham Health Partners, Birmingham, UK; National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK. Electronic address: a.denniston@bham.ac.uk.

The Lancet. Digital health. 2021;(1):e51-e66
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Abstract

Health data that are publicly available are valuable resources for digital health research. Several public datasets containing ophthalmological imaging have been frequently used in machine learning research; however, the total number of datasets containing ophthalmological health information and their respective content is unclear. This Review aimed to identify all publicly available ophthalmological imaging datasets, detail their accessibility, describe which diseases and populations are represented, and report on the completeness of the associated metadata. With the use of MEDLINE, Google's search engine, and Google Dataset Search, we identified 94 open access datasets containing 507 724 images and 125 videos from 122 364 patients. Most datasets originated from Asia, North America, and Europe. Disease populations were unevenly represented, with glaucoma, diabetic retinopathy, and age-related macular degeneration disproportionately overrepresented in comparison with other eye diseases. The reporting of basic demographic characteristics such as age, sex, and ethnicity was poor, even at the aggregate level. This Review provides greater visibility for ophthalmological datasets that are publicly available as powerful resources for research. Our paper also exposes an increasing divide in the representation of different population and disease groups in health data repositories. The improved reporting of metadata would enable researchers to access the most appropriate datasets for their needs and maximise the potential of such resources.

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Publication Type : Review

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