Diabetic retinopathy (DR) is the main ocular complication of diabetes mellitus and is an eye disease with a significant impact on global health. The way diabetic retinopathy is managed has already been revolutionised in the early part of the 21st century, thanks to major advances in diagnostics, in technologies and in the treatment of pathology. For example, the accessibility of optical coherence tomography imaging and the development of anti-VEGF treatment (vascular endothelial growth factor) are just some of the developments that have made the history of the new management of DR. However, there is even more exciting progress underway and, looking ahead to 2030many of these ongoing developments are likely to continue to transform the field of diabetic retinopathy.
Epidemiological changes
The global prevalence of DR is expected to increase significantly in the coming decades, from about 103 million in 2020, to 130 million in 2030 and 161 million in 2045. These projections are due to a variety of factors, including the increasing prevalence of diabetes worldwide, changes in lifestyle, increasing average lifespan and an ageing world population. The incidence of diabetic macular oedema (EMD) is also expected to increase by about 25%, or 24 million patients by 2030. These changes will mainly affect low-income regions such as the Western Pacific, Central and South America, Asia, Africa, the Middle East and North Africa. This geographical shift will lead to very high costs and screening programmes targeting all patients with diabetes will be needed to enable early diagnosis and treatment and reduce rates of vision loss.
Non-vascular aspects of DR
Clinically visible retinal lesions associated with DR, such as microaneurysms and haemorrhages, are mainly the result of retinal microvascular damage. Consequently, the focus on pathophysiology, diagnosis and clinical evaluation has traditionally been on the vascular aspect of the disease. However, with the availability of improved structural retinal imaging modalities and functional assessments, data have accumulated over the years on significant neural dysfunction of the retina, which occurs along with, or in some cases precedes, the development of vascular abnormalities. These structural and functional changes have been termed 'diabetic retinal neurodegeneration' (DRN). Retinal thinning is progressive and may precede the development of the clinically visible lesions of the disease.
However, although this neural involvement is now clear, many questions remain about the prognostic significance of DRN, its impact on quality of life and clinical assessment. Recently, a portable chromatic pupillometer has been introduced that has been shown to be able to provide rapid clinical assessment of retinal neural function in diabetes. Applications, however, will need to be replicated and validated in larger cohorts.
New imaging techniques
Newer imaging techniques have made it possible to obtain a wealth of information, thanks to imaging modalities such as widefield systems and optical computed tomography (OCT). In particular, new imaging modalities such as ultra-widefield retinal imaging (UWF) and OCT angiography (OCTA) have been available for research and clinical use for a number of years. Their most important advantage is that they provide a much larger retinal surface image for the assessment of peripheral retinal structures than standard colour fundus oculi (CFP) photography. In the future, the applications of these modern imaging techniques will be expanded and their limitations improved.
The role of artificial intelligence
Artificial intelligence (AI) and deep learning (DL) will play an increasingly important role in the next decade in medicine, for diagnosis, screening, prognosis and disease management. Ophthalmology has been a leader in the development of artificial intelligence algorithms for clinical use and diagnosis, particularly in DR, starting with CFP images in 2016. To date, several years later, there are many AI-based systems available for RD screening that have been approved for clinical use. The introduction of AI algorithms as tools to assist in large-scale DR screening will be associated with significant cost savings in the future. It is likely that by 2030 we will see artificial intelligence algorithms routinely implemented in many large-scale screening programmes worldwide, either as fully autonomous systems or in hybrid systems where the algorithms function as assistive tools.
In addition to DR diagnosis and screening, there are other potential uses of AI that are being developed. For example, algorithms could help in image detection in imaging and clinical data processing.
New in classification
As a consequence of these many exciting advances in the field of DR, our classification and staging systems will have to be updated. In fact, the ETDRS (Early Treatment Diabetic Retinopathy Study) and the ICDR (International Classification of Diabetic Retinopathy), currently in use, have been in use for many years and have significant limitations.
Some of the key areas that will need to be reviewed in an update of classification systems include, for example:
- the inclusion of relevant prognostic data derived from UWF systems;
- the recognition and evaluation of non-vascular aspects of diabetic retinal disease, such as retinal neural dysfunction (DRN);
- the use of information and biomarkers derived from newly available imaging modalities, such as OCT and OCTA;
- greater consideration for diabetic macular oedema;
- accurate prognosis following treatment.
Currently, major international efforts are underway to update the DR classification system.
English 
Italian