Abstract
Purpose :
In Scotland, optometrists capture millions of retinal images every year as part of routine eye examination appointments, representing a unique opportunity to create a large-scale longitudinal retinal image resource for research, such as for the development of new technologies for early prediction of eye disease. The Scottish Collaborative Optometry-Ophthalmology Network e-research (SCONe) was established to build such a repository and to examine whether images from community optometry practices could be collated in a protected safe storage environment (National Safe Haven; NSH) and linked to national healthcare records.
Methods :
Images and associated metadata (required for linkage within the NSH) were extracted from 5 practices and from patients more than 60 years old. A systematic approach was developed to integrate, link, and clean data from different sources within each of the practices (e.g., image capture devices and practice management systems). Data was delivered to the NSH for linkage to national healthcare records, patient characteristics and image quality was assessed.
Results :
31,280 images from 4,132 patients captured over a 16-year period was delivered and linked to national healthcare records within the NSH. Visual inspection of image quality showed 61% suitable for computerised image analysis, 26% were low quality, and 13% anterior eye photographs. Patients had several ocular (macular degeneration, diabetic retinopathy, glaucoma, macular oedema and retinal detachment) and systemic (diabetes, hypertension, mild cognitive impairment) conditions. The initial cohort included 20,887 images which predated the hospital diagnoses of these conditions.
Conclusions :
Our results demonstrate the feasibility of collecting retinal images from community optometry practice and linking them to other routinely collected healthcare data within a safe storage environment. Initial findings suggests that the dataset contains images with early features of disease that could be used to develop early prediction technologies. The successful development of image extraction, linkage and secure storage protocols will allow the number of linked images to increase markedly over the next two years.
This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.