AI Screening for Diabetic Retinopathy

NCT ID: NCT05704491

Last Updated: 2024-07-11

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 100 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-01-30
• Study Completion Date: 2025-12-31

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.

Conditions

Diabetes Mellitus

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: OTHER

Study Groups

K+A+

diabetic retinopathy according to AI present (K+) AND diabetic retinopathy according to the doctor present (A+)

artificial intelligence (AI) algorithm of the MONA DR model

Intervention Type: DIAGNOSTIC_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-

diabetic retinopathy according to AI present (K+) AND diabetic retinopathy according to the doctor absent (A-)

artificial intelligence (AI) algorithm of the MONA DR model

Intervention Type: DIAGNOSTIC_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+

diabetic retinopathy according to AI absent (K-) AND diabetic retinopathy according to the doctor present (A+)

artificial intelligence (AI) algorithm of the MONA DR model

Intervention Type: DIAGNOSTIC_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-

diabetic retinopathy according to AI absent (K-) AND diabetic retinopathy according to the doctor absent (A-)

artificial intelligence (AI) algorithm of the MONA DR model

Intervention Type: DIAGNOSTIC_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.

Interventions

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

West German Center of Diabetes and Health

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

West German Center of Diabetes and Health

Düsseldorf, , Germany

Site Status: RECRUITING

Countries

Germany

Central Contacts

Stephan Martin, MD

Role: CONTACT

stephan.martin@uni-duesseldorf.de

+49-2115660360 ext. 70

Kerstin Kempf, PhD

Role: CONTACT

kerstin.kempf@wdgz.de

+49-2115660360 ext. 16

Facility Contacts

Stephan Martin, MD

Role: primary

stephan.martin@uni-duesseldorf.de

+49(0)211-56 60 360 71

Kerstin Kempf, PhD

Role: backup

kerstin.kempf@wdgz.de

+49(0)211-56 60 360 16

Other Identifiers

AimdR

Identifier Type: -

Identifier Source: org_study_id