NCT05704491

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.

Trial Health

57
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
100

participants targeted

Target at P50-P75 for all trials

Timeline
Completed

Started Jan 2023

Typical duration for all trials

Geographic Reach
1 country

1 active site

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

January 19, 2023

Completed
11 days until next milestone

First Posted

Study publicly available on registry

January 30, 2023

Completed
Same day until next milestone

Study Start

First participant enrolled

January 30, 2023

Completed
1.9 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 31, 2024

Completed
1 year until next milestone

Study Completion

Last participant's last visit for all outcomes

December 31, 2025

Completed
Last Updated

July 11, 2024

Status Verified

July 1, 2024

Enrollment Period

1.9 years

First QC Date

January 19, 2023

Last Update Submit

July 10, 2024

Conditions

Diabetes Mellitus

Outcome Measures

Primary Outcomes (4)

  • PPV

    positive predictive value

    12 months

  • NPV

    negative predictive value

    12 months

  • SEN

    sensitivity

    12 months

  • SPEZ

    specificity

    12 months

Study Arms (4)

K+A+

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

K+A-

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

K-A+

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

K-A-

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

Interventions

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.

K+A+K+A-K-A+K-A-

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
Study Population

Patients of the West German Centre of Diabetes and Health with Type 2 Diabetes

You may qualify if:

  • 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

You may not qualify if:

  • History of laser treatment
  • Contraindication to the fundus imaging systems used in the study

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

West German Center of Diabetes and Health

Düsseldorf, 40591, Germany

RECRUITING

MeSH Terms

Conditions

Diabetes Mellitus

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Glucose Metabolism DisordersMetabolic DiseasesNutritional and Metabolic DiseasesEndocrine System Diseases

Intervention Hierarchy (Ancestors)

AlgorithmsMathematical Concepts

Central Study Contacts

Stephan Martin, MD

CONTACT

+49-2115660360stephan.martin@uni-duesseldorf.de

Kerstin Kempf, PhD

CONTACT

+49-2115660360kerstin.kempf@wdgz.de

Study Design

Study Type
observational
Observational Model
CASE CONTROL
Time Perspective
OTHER
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

January 19, 2023

First Posted

January 30, 2023

Study Start

January 30, 2023

Primary Completion

December 31, 2024

Study Completion

December 31, 2025

Last Updated

July 11, 2024

Record last verified: 2024-07

Data Sharing

IPD Sharing
Will not share

Locations

DE(1)