Developing Artificial Intelligence Approaches to Predicting Progressive Myopia and Risk of Myopic Complications Based on Optometry Data

Worldwide, uncorrected refractive error is the leading cause of visual impairment, affecting 116.3 million people. Myopia (short-sightedness) is the most common disorder, and its prevalence is increasing as we witness a 'myopia epidemic'. In 2010, 1.9 billion people, 27% of the world's population, were myopic, with 70 million (2.8%) highly myopic. It is estimated that these percentages will increase to 52% and 10% respectively by 2050 (Fricke et al., 2018). Visual impairment from myopia has a significant economic impact and adverse effect on quality of life, with pathologic myopia particularly harmful as it leads to degenerative changes at the back of the eye causing blindness.

Myopia is a risk factor for cataract, glaucoma, retinal detachment and myopic macular degeneration. It is estimated that up to 11% of people with pathological myopia develop choroidal neovascularization and axial elongation can cause distortion of the peripapillary region leading to glaucoma and loss of visual field (Wong et al., 2014).

Randomized control trials have shown interventions (e.g. bifocal contact lenses, low-dose atropine) can slow progression of childhood myopia (Chamberlain et al., 2019) and earlier interventions are likely to be more effective. However, at present there is no satisfactory way to identify individuals at risk of progressive myopia or to identify those most likely to benefit from treatment intervention. The ability to better determine risk would enable treatments to be targeted for those most likely to benefit, increasing success rates, and giving optometrists confidence to increase their use of innovative treatments. If it is possible to better identify individuals at risk of myopic changes, there is greater opportunity for successful preventative treatment.

The project will utilise the Scottish Clinical Optometry and Ophthalmology Network e-research (SCONe) collaboration, a research repository developed by academic partners at Glasgow Caledonian University and University of Edinburgh. In Scotland, over 1 million retinal images are captured by optometrists each year, providing a rich population-based resource for research. SCONe is utilizing images from optometry practices to build a curated dataset, incorporating retinal photographs and optical coherence tomography (OCT) images linked to clinical information. This is a growing, longitudinal resource, ideal for developing Artificial Intelligence (AI) tools to improve clinical decision making.

The main aim of this project is to use SCONe to develop AI algorithms that can be used in optometric practices to identify individuals at high risk of myopic progression based on information from retinal photographs, OCT, refraction, and other clinical features. Within a busy practice, optometrists may be missing opportunities to treat those likely to benefit from preventative treatments, and an AI tool could facilitate identification of suitable individuals. The objective of developing such a tool would be to categorize eyes as high, medium, or low risk, or attribute an individualized score indicating the likelihood of progressive changes, i.e., a Myopia Progression Index (MPI). The longitudinal nature of the SCONe dataset, and its focus on data acquired from primary care optometry (i.e. high street opticians' practices) from across Scotland, provides a unique opportunity for this project.
A secondary aim is to develop and validate an additional AI algorithm to determine axial length and refraction from features of retinal photographs alone and to test the algorithm in its ability to differentiate patients with myopia, high myopia and pathological myopia from healthy individuals. This second algorithm, again utilising the SCONe dataset, will aim to quantify retinal features already known to be associated with myopia; for example, signs of peripapillary atrophy; and facilitate identification of new retinal biomarkers of myopia, for example, changes in patterns of retin