AI Screening for Diabetic Retinopathy

Study Overview
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Brief Summary
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The increasing prevalence of diabetes mellitus represents a major health problem, especially since around 40% of diabetic patients develop diabetic retinopathy, which severely impairs vision and can lead to blindness. This development could be prevented by annual check-ups and timely referral for treatment. However, there are major differences in the quality of examinations and bottlenecks in examination appointments. A solution to the problem could be the use of artificial intelligence (AI), especially deep learning. Initial studies have shown that deep learning algorithms can be used successfully to detect diabetic retinopathy. However, it remains to be clarified whether the use of AI can achieve a sufficiently high level of accuracy in the detection of retinopathies. Therefore, in the present study, the positive predictive value (PPV), the negative predictive value (NPV), the sensitivity (SEN) and the specificity (SPEZ) of the AI algorithm 'MONA-DR-Model' in the detection of diabetic retinopathy should be measured. In addition, it is to be examined how well the classification into mild and severe retinopathy corresponds and how well this new examination method is accepted by the patients.

Detailed Description
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As part of the study, a 45-degree fundus image is taken for each eye and patient using the 'Crystalvue NFC 600'. The fundus photographs are then analyzed using the 'MONA-DR-Mode'l and classified as diabetic retinopathy according to AI present (K+) or diabetic retinopathy according to AI absent (K-). These classifications are compared with the results (diabetic retinopathy according to the doctor present (A+) or diabetic retinopathy according to the doctor absent (A-)) of the examinations routinely provided for in the Disease Management Program (DMP) diabetes mellitus type 2 by resident ophthalmologists who work in the period 6 months before and after the fundus photography in the West German Centre of Diabetes and Health (WDGZ) were compared. All patients with the assessment diabetic retinopathy according to AI present (K+) or discrepancies with the ophthalmological DMP examination in the outpatient environment are offered a routine appointment at the Marienhospital. There, an eye examination is then carried out by an ophthalmologist and, without knowledge of the previous findings, a reassessment and classification as diabetic retinopathy according to the doctor present (A+) or diabetic retinopathy according to the doctor absent (A-) is carried out by the AI.

Official Title
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Accuracy of an AI Model for Diabetic Retinopathy Screening in Real-life

Conditions
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Diabetes Mellitus

Intervention / Treatment
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* Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR model


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Diagnosis of diabetes mellitus Diabetes duration ≥ 5 years Age > 18 years old Patient is able to give informed consent Fluent in written and spoken German, or interpreter present Exclusion Criteria: History of laser treatment Contraindication to the 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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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| K+A+<br>diabetic retinopathy according to AI present (K+) AND diabetic retinopathy according to the doctor present (A+) | Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR model<br>* A 45-degree fundus image is taken for each eye and patient using the Crystalvue NFC 600. The fundus photographs are then analyzed using the MONA DR model and classified for presence of diabetic retinopathy.<br>|
| K+A-<br>diabetic retinopathy according to AI present (K+) AND diabetic retinopathy according to the doctor absent (A-) | Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR model<br>* A 45-degree fundus image is taken for each eye and patient using the Crystalvue NFC 600. The fundus photographs are then analyzed using the MONA DR model and classified for presence of diabetic retinopathy.<br>|
| K-A+<br>diabetic retinopathy according to AI absent (K-) AND diabetic retinopathy according to the doctor present (A+) | Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR model<br>* A 45-degree fundus image is taken for each eye and patient using the Crystalvue NFC 600. The fundus photographs are then analyzed using the MONA DR model and classified for presence of diabetic retinopathy.<br>|
| K-A-<br>diabetic retinopathy according to AI absent (K-) AND diabetic retinopathy according to the doctor absent (A-) | Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR model<br>* A 45-degree fundus image is taken for each eye and patient using the Crystalvue NFC 600. The fundus photographs are then analyzed using the MONA DR model and classified for presence of diabetic retinopathy.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| PPV | positive predictive value | 12 months | 
| NPV | negative predictive value | 12 months | 
| SEN | sensitivity | 12 months | 
| SPEZ | specificity | 12 months |