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AI Screening for Diabetic Retinopathy

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

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05704491
Acronym
AimdR
Enrollment
100
Registered
2023-01-30
Start date
2023-01-30
Completion date
2027-12-31
Last updated
2026-08-10

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Diabetes Mellitus

Brief 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.

Detailed description

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.

Interventions

DIAGNOSTIC_TESTartificial 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

West German Center of Diabetes and Health
Lead SponsorOTHER

Study design

Observational model
CASE_CONTROL
Time perspective
OTHER

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

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

Design outcomes

Primary

MeasureTime frameDescription
PPV12 monthspositive predictive value
NPV12 monthsnegative predictive value
SEN12 monthssensitivity
SPEZ12 monthsspecificity

Countries

Germany

Contacts

CONTACTStephan Martin, MD
stephan.martin@uni-duesseldorf.de+49-2115660360
CONTACTKerstin Kempf, PhD
kerstin.kempf@wdgz.de+49-2115660360

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Aug 11, 2026