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AI Screening for Diabetic Retinopathy

NCT05704491 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 100

Last updated 2024-07-11

No results posted yet for this study

Summary

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.

Conditions

Interventions

DIAGNOSTIC_TEST

artificial intelligence (AI) algorithm of the MONA DR model

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.

Sponsors & Collaborators

  • West German Center of Diabetes and Health

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-01-30
Primary Completion
2024-12-31
Completion
2025-12-31

Countries

  • Germany

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05704491 on ClinicalTrials.gov