AI Screening for Diabetic Retinopathy (AimdR)

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.

Study Overview

• Status: Recruiting
• Conditions: Diabetes Mellitus
• Intervention / Treatment: Diagnostic test: artificial intelligence (AI) algorithm of the MONA DR model

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 Type: Observational
• Enrollment (Estimated): 100

Contacts and Locations

• Study Contact: Name: Stephan Martin, MD; Phone Number: 70 +49-2115660360; Email: stephan.martin@uni-duesseldorf.de
• Study Contact Backup: Name: Kerstin Kempf, PhD; Phone Number: 16 +49-2115660360; Email: kerstin.kempf@wdgz.de

Study Locations

• Germany
  • Düsseldorf, Germany, 40591
    • Recruiting
    • West German Center of Diabetes and Health
    • Principal Investigator: Stephan Martin, MD
    • Contact: Stephan Martin, MD; Phone Number: +49(0)211-56 60 360 71; Email: stephan.martin@uni-duesseldorf.de
    • Contact: Kerstin Kempf, PhD; Phone Number: +49(0)211-56 60 360 16; Email: kerstin.kempf@wdgz.de

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

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

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Control
• Time Perspectives: Other

Number of groups / cohorts

4

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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; 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-)	Diagnostic test: 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.
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; 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-)	Diagnostic test: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
PPV	positive predictive value	12 months
NPV	negative predictive value	12 months
SEN	sensitivity	12 months
SPEZ	specificity	12 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
patients preferences	questionnaire for patients preferences and satisfaction concerning the AI-supported eye examination	12 months

Collaborators and Investigators

• Sponsor: West German Center of Diabetes and Health

Study record dates

Study Major Dates

• Study Start (Actual): January 30, 2023
• Primary Completion (Estimated): December 31, 2024
• Study Completion (Estimated): December 31, 2025

Study Registration Dates

• First Submitted: January 19, 2023
• First Submitted That Met QC Criteria: January 19, 2023
• First Posted (Actual): January 30, 2023

Study Record Updates

• Last Update Posted (Actual): July 11, 2024
• Last Update Submitted That Met QC Criteria: July 10, 2024
• Last Verified: July 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Cardiovascular Diseases; Vascular Diseases; Eye Diseases; Endocrine System Diseases; Diabetic Angiopathies; Diabetes Complications; Diabetes Mellitus; Retinal Diseases; Diabetic Retinopathy
• Other Study ID Numbers: AimdR

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No