The increase of diabetes patients is a 21st century global health challenge with a predicted
642 million people suffering from the disease by 2040. Diabetes mellitus is characterized by
high blood sugar levels over a prolonged period of time. These uncontrolled blood sugar
levels can damage the inner lining of blood vessels which on the long term causes
microvascular complications that affect small blood vessels. Retinopathy is the most
prevalent microvascular complication of diabetes and is caused by small blood vessel damage,
and neural damage at the back layer of the eye, the retina.
Diabetic retinopathy (DR) is the leading cause of blindness and visual disability in the
working population. According to a study of the Eye Diseases Prevalence Research group, 40%
of adult diabetes patients in the United States have some degree of DR and 8% have
vision-threatening forms of DR. In addition, the DR Barometer study indicated that many
patients with diabetes do not have a regular appointment with ophthalmology for an eye
examination. Risk of vision loss can be significantly decreased with annual retinal screening
and detection of cases that need to be referred for follow-up and treatment. The best example
showing the value of eye screening is from the United Kingdom (UK). As a result of an
implementation of a nationwide screening program, DR is no longer the leading cause of
irreversible blindness in the UK.
In Flanders, and in Belgium as a whole, no such well-organized, nationwide DR screening
program is in place and the approach is more fragmented. Flemish guidelines for diabetes care
recommend an annual visit to the ophthalmologist for all the diabetic patients who receive
insulin therapy in order to check if they have DR. About 30% of the diabetics will be
diagnosed for DR and 70% are disease free or in a very early stage that doesn't need further
treatment. However, manual detection of DR performed by an occupied, scarce ophthalmologist
is labor-intensive and expensive, causing long waiting times for the patient and possibly
resulting in a lack of care when needed.
Given the extent of the diabetes population in Flanders it is self-evident that there are
difficulties to screen all patients in a timely manner by ophthalmologists. Indeed, a large
amount of diabetes type 2 patients do not follow the annual referral by their general
practitioner (GP) and are therefore screened at a too late stage, resulting in high,
avoidable costs for the patient and society. Even more, the screening of the diabetic
patients by an ophthalmologist put a resource burden on our healthcare system. Task
differentiation, where trained graders or GP's instead of ophthalmologists grade for
referable DR, can offer a solution for the too long waiting times and the high cost.
Nevertheless, manual grading of DR still is labor-intensive and costly. Even more, despite
the implementation of nationwide screening programs for DR and their accompanying grading
protocols, there is still substantial room for improvement in the accuracy of manual DR
grading.
Recently, deep learning (DL), a form of artificial intelligence (AI), has been introduced for
automated analysis of images. In a landmark paper, Gulshan and co-workers published on a deep
learning algorithm with high sensitivity and specificity for detecting referable DR. This
study paved the way for further developments in the field of deep learning for automated DR
detection, resulting in DL models that achieve specialist-level accuracy in diagnosing DR
severity. IDx, for example, obtained the first-ever FDA authorization for an AI diagnostic
system in any field of medicine for DR detection.
Implementation of software for automated analysis is seen as a cost-effective solution to
support decision-making in an eye screening program. In the study by Tufail et al. three
different AI grading tools were retrospectively compared for their performance and
cost-effectiveness in the DR screening program in the UK. In a follow-up study by Heydon et
al. the most promising AI grading tool was prospectively evaluated for use in the UK
screening program, demonstrating high sensitivity with a specificity that could halve the
workload of the manual graders.
Despite recent research there is still an existing gap for AI to be implemented effectively
and efficiently in DR screening programs. For example, the high false-positive rate of AI
based results hamper the clinical workflow. Also important to note is that DL models cannot
replace the breadth and contextual knowledge of human specialists. It is the case that even
the most accurate models will still need to be implemented into an existing clinical workflow
before they can improve patient care at all. Besides, the real-world uptake of AI
applications is slow and this is partly due to a lack of convincing evidence of the
economical impact.
Taken all together, renewal within diabetes care in Flanders, and more in particular further
development of a more efficient DR screening pathway, is necessary to ensure that the
accessibility and quality of diabetic eye care can be guaranteed at manageable costs.
Flanders can undoubtedly benefit from a more efficient and cost-effective AI-assisted DR
screening workflow that is at least as accurate as a human specialist. Note that the
translation of study results abroad to the Flanders situation is limited. After all, one
cannot simply assume that cost-effectiveness ratios from foreign economic evaluations also
apply in the Flanders context. Meaning that policymakers cannot base their decisions on the
possible introduction of preventive screening interventions in Flanders directly on foreign
studies. These findings demonstrate the clear need to set up a specific research project in
Flanders to evaluate the efficiency and cost-effectiveness of a tailor-made DR screening
program in Flanders.