Intelligent Evaluation of Diabetic Retinopathy

Study Overview
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Brief Summary
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Brief Summary: The main objective of EviRed project is to develop and validate a system assisting the ophthalmologist by improving prediction of evolution, and decision making during diabetic retinopathy (DR) follow-up. It will replace the current classification of diabetic retinopathy (DR) which provides an insufficient prediction precision. It will use modern available fundus imaging devices and artificial intelligence (AI) to properly integrate the amount of data they provide with other medical data of the patient. A cohort of 5000 diabetic patients will be recruited and followed for an average of 2 years in order to collect data to train and validate the new prediction system.

Detailed Description
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A cohort of 5,000 diabetic patients with different stages of DR will be recruited and followed for an average of 2 years. Each year, general data as well as ophthalmological data will be collected. Retinal images and videos of both eyes will be acquired using different imaging modalities including ultrawidefield photography, OCT and OCT angiography. The EviRed cohort will be split in two groups: one group of 1,000 patients (validation cohort) will be randomly selected during the inclusion period by unbalanced draw to be representative of the general diabetic population. Their data will be used for the validation of the algorithms. The data of the remaining 4,000 patients (training cohort) will be used to train the algorithms. The main objective will be the validation of the prognostic tool and evaluate how accurately the algorithm can predict progression to severe retinopathy in the following year. Secondary objectives will be to evaluate how accurately the algorithm can assess DR severity and individual components of DR complications as well as to compare prediction by algorithm to that made by ophthalmologists based on the current DR classification

Official Title
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Intelligent Evaluation of Diabetic Retinopathy

Conditions
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Diabetic Retinopathy

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: patient with type 1 or type 2 diabetes or other, aged 18 years or more, diabetes duration greater than 10 years for type 1 diabetes, no previous vitrectomy, patient affiliated to social security, women of childbearing potential who are unwilling or unable to use a method to avoid pregnancy; women who are pregnant or breastfeeding can be included in the study. Exclusion Criteria: ungradable fundus photography or OCT/OCTA imaging, previous treatment with vitrectomy, participant is unable or unwilling to comply with study procedures (including foreign language speakers who are not assisted by a native language speaker), vulnerable participants (minors, legally detained), patients under legal protection (guardianship), prisoners or subjects who are involuntarily incarcerated

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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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Progression to severe retinopathy | Progression towards severe diabetic retinopathy form. | month 12 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Algorithm performance | Algorithms will be evaluated by comparing the algorithm performance to automatically assess diabetic retinopathy severity as well as individual components of severe retinopathy against the same grading made by human graders. | 3 years | 
| Comparison of algorithms prediction to human prediction. | at each patient's visit of the validation cohort, ophthalmologists will evaluate the risk of DR progression based on the current DR classification and their clinical experience. The risk of DR will be expressed by the clinician as a continuous variable (its estimated probability of progression) or as a semi-quantitative variable. Performance of the human prediction will be compared to the algorithm using sensitivity, specificity and AUC. | 3 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Diabetic retinopathy. ., Diabetes, Artificial intelligence., Deep learning