Diabetes MellitusAimdRNCT05704491
AI Screening for Diabetic Retinopathy
An observational study of Diabetes Mellitus, run by West German Center of Diabetes and Health at 1 site in Germany.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Diabetes Mellitus
- What is being tested?
- artificial intelligence (AI) algorithm of the MONA DR model
- How many people
- 100 participants
- Where
- 1 siteGermany
- Study dates
- 30 Jan 2023to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients of the West German Centre of Diabetes and Health with Type 2 Diabetes
Who may be able to join
- 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
Who cannot join
- History of laser treatment
- Contraindication to the fundus imaging systems used in the study
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Germany1 site, 1 recruiting
- Recruiting
West German Center of Diabetes and Health
Düsseldorf, 40591
Stephan Martin, MD, +49(0)211-56 60 360 71, stephan.martin@uni-duesseldorf.de
Kerstin Kempf, PhD, +49(0)211-56 60 360 16, kerstin.kempf@wdgz.de
About this study
Official title: Accuracy of an AI Model for Diabetic Retinopathy Screening in Real-life
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.
Read the 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.
- Conditions
- Diabetes Mellitus
- Being tested
- artificial intelligence (AI) algorithm of the MONA DR model
- Medical subject headings
- Diabetes Mellitus
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
artificial intelligence (AI) algorithm of the MONA DR modelDiagnostic test
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 group | Role | What they receive |
|---|---|---|
| K+A+ | diabetic retinopathy according to AI present (K+) AND diabetic retinopathy according to the doctor present (A+) Receives: Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR model | |
| K+A- | diabetic retinopathy according to AI present (K+) AND diabetic retinopathy according to the doctor absent (A-) Receives: Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR model | |
| K-A+ | diabetic retinopathy according to AI absent (K-) AND diabetic retinopathy according to the doctor present (A+) Receives: Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR model | |
| K-A- | diabetic retinopathy according to AI absent (K-) AND diabetic retinopathy according to the doctor absent (A-) Receives: Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR model |
What is being measured
The primary outcome is the main question the study is designed to answer.
PPV
positive predictive value
Measured: 12 months
NPV
negative predictive value
Measured: 12 months
SEN
sensitivity
Measured: 12 months
SPEZ
specificity
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jan 2023
- Study start30 Jan 2023
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Diabetes MellitusStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Other
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- West German Center of Diabetes and Health
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05704491
- Sponsor's protocol ID
- AimdR
- Acronym
- AimdR
- Status last verified
- Aug 2026
- First submitted
- 19 Jan 2023
- Last updated
- 10 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 10 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Accuracy of an AI Model for Diabetic Retinopathy Screening in Real-life
- ClinicalTrials.gov ID
- NCT05704491
- Acronym / sponsor ID
- AimdR / AimdR
- Conditions
- Diabetes Mellitus
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- West German Center of Diabetes and HealthUniversity, hospital or other
- Study start
- 30 Jan 2023 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 30 Jan 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Other
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| K+A+ | – | Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR modeldiabetic retinopathy according to AI present (K+) AND diabetic retinopathy according to the doctor present (A+) |
| K+A- | – | Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR modeldiabetic retinopathy according to AI present (K+) AND diabetic retinopathy according to the doctor absent (A-) |
| K-A+ | – | Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR modeldiabetic retinopathy according to AI absent (K-) AND diabetic retinopathy according to the doctor present (A+) |
| K-A- | – | Diagnostic Test: artificial intelligence (AI) algorithm of the MONA DR modeldiabetic retinopathy according to AI absent (K-) AND diabetic retinopathy according to the doctor absent (A-) |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients of the West German Centre of Diabetes and Health with Type 2 Diabetes
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (2 listed)
- History of laser treatment
- Contraindication to the fundus imaging systems used in the study
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| artificial intelligence (AI) algorithm of the MONA DR modelA 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. | Diagnostic test | K+A+, K+A-, K-A+, K-A- |
Primary outcome
PPV
Time frame: 12 months
positive predictive value
NPV
Time frame: 12 months
negative predictive value
SEN
Time frame: 12 months
sensitivity
SPEZ
Time frame: 12 months
specificity
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Aug 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetes Mellitus, run by West German Center of Diabetes and Health at 1 site in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 10 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Stephan Martin, MD (+49-2115660360 ext. 70, stephan.martin@uni-duesseldorf.de), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
PPV (time frame: 12 months). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Aug 2026.