AI Screening for Diabetic Retinopathy

Condition(s) studied

Investigational drug(s) / intervention(s)

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.

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.

Eligibility

Primary outcome measure(s)

• PPV — 12 months
  positive predictive value
• NPV — 12 months
  negative predictive value
• SEN — 12 months
  sensitivity
• SPEZ — 12 months
  specificity

Trial sites (1)

More West German Center of Diabetes and Health trials in Germany

Other trials for the same condition