E-CLAIR: Efficiency and Cost-effectiveness of Artificial Intelligence Based Diabetic Retinopathy Screening in Flanders

Study Overview
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Brief Summary
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To evaluate the efficiency and cost-effectiveness of an artificial intelligence based diabetic retinopathy screening program in Flanders

Detailed Description
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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.

Official Title
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E-CLAIR: Efficiency and Cost-effectiveness of Artificial Intelligence Based Diabetic Retinopathy Screening in Flanders

Conditions
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Diabetic Retinopathy

Intervention / Treatment
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* Device: deep learning
* Diagnostic Test: remote grading of fundus images
* Diagnostic Test: gold standard


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Diagnosis of diabetes mellitus Age > 18 years old Patient is capable of giving informed consent Fluent in written and oral Dutch, or interpreter present Exclusion Criteria: - History of treatment for diabetic retinopathy or diabetic macular edema (laser or intravitreal injections) Participant is contraindicated for imaging by fundus imaging systems used in the study

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: current workflow in Flanders<br>patient visits ophthalmologist | Diagnostic Test: gold standard<br>* examination by ophthalmologist<br>|
| Active Comparator: AI-only workflow<br>patient is imaged, images are interpreted by DR AI tool, only referrable cases identified by DR AI tool will visit ophthalmologist | Device: deep learning<br>* a form of artificial intelligence (AI), has been introduced for automated analysis of images<br>|
| Active Comparator: AI-human workflow<br>patient is imaged, images are interpreted by DR AI tool, referrable cases identified by DR AI tool will be remotely graded by a human, only the high risk patients will visit ophthalmologist | Diagnostic Test: remote grading of fundus images<br>* referrable cases identified by DR AI tool will be remotely graded by a human<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| sensitivity | To evaluate the efficiency of the use of AI in screening for DRP: sensitivity | 6 months | 
| specificity | To evaluate the efficiency of the use of AI in screening for DRP: specificity | 6 months | 
| AUC | To evaluate the efficiency of the use of AI in screening for DRP: AUC | 6 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| precision | performance of three DR screening workflows: precision | 6 months | 
| decision tree model | cost-effectiveness of three DR screening workflows: decision tree model | 6 months | 
| recall | performance of three DR screening workflows : recall | 6 months | 
| F1 score | performance of three DR screening workflows: F1 score | 6 months | 
| false positives and false negatives | performance of three DR screening workflows: false positives and false negatives | 6 months |