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NCT Number: NCT05704491

AI Screening for Diabetic Retinopathy

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.

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Key information

Conditions

Diabetes Mellitus Endocrine System Diseases Glucose Metabolism Disorders Metabolic Diseases Nutritional and Metabolic Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

artificial intelligence (AI) algorithm of the MONA DR model

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.

Primary outcomes

  1. PPV

    Time frame: 12 months

    positive predictive value

  2. NPV

    Time frame: 12 months

    negative predictive value

  3. SEN

    Time frame: 12 months

    sensitivity

  4. SPEZ

    Time frame: 12 months

    specificity

Study contacts

Contact information is provided by the study sponsor or research team.

Kerstin Kempf, PhD

CONTACT

[email protected]

+49-2115660360 ext. 16

Stephan Martin, MD

CONTACT

[email protected]

+49-2115660360 ext. 70

Sponsors and collaborators

Lead sponsor

West German Center of Diabetes and Health

Other

Registry information

Official study title

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

Acronym: AimdR

Important dates

Study start
2023
Primary completion
2024
Study completion
2025
First posted
Jan 30, 2023
Registry last updated
Jul 11, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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