AEYE-DS Software for Diabetic Retinopathy

Recruiting at 2 trial locations

Overseen By Ahava Stein

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: AEYE Health Inc

Must not be taking: Photosensitivity drugs

Disqualifiers: Vision loss, Macular edema, Retinopathy, Retinal surgery, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

Duration: 1 day

363 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial is testing a computer program called AEYE-DS that helps doctors find early signs of eye damage in diabetic patients by looking at pictures of their eyes. The goal is to make it easier for these patients to get regular eye check-ups.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. However, if you are taking medication that causes photosensitivity (sensitivity to light), it might be a concern for participation.

How does AEYE-DS Software differ from other treatments for diabetic retinopathy?

AEYE-DS Software is unique because it uses artificial intelligence to automatically analyze retinal images for signs of diabetic retinopathy, potentially reducing the need for human interpretation and making screening more accessible and efficient. ^{1 2 3 4 5}

What data supports the effectiveness of the AEYE-DS Software treatment for diabetic retinopathy?

Research shows that artificial intelligence (AI) software, like AEYE-DS, can effectively screen for diabetic retinopathy by accurately interpreting eye images, similar to human experts. Studies indicate that AI tools have high sensitivity and specificity, meaning they are good at correctly identifying those with and without the condition. ^{3 6 7 8 9}

Are You a Good Fit for This Trial?

Inclusion Criteria

Understand the study and volunteer to sign the informed consent

You have been diagnosed with diabetes according to the American Diabetes Association and World Health Organization criteria.

You are 22 years old or older.

Exclusion Criteria

You have been diagnosed with diabetic retinopathy in the past.

Participant has a condition that, in the opinion of the investigator, would preclude participation in the study (e.g., unstable medical status including blood pressure or glycemic control, microphthalmia or previous enucleation)

Participant is contraindicated for imaging by fundus imaging systems used in the study

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants undergo screening for diabetic retinopathy using the AEYE-DS software device

1 day

1 visit (in-person)

Follow-up

Participants are monitored for safety and effectiveness after the use of the AEYE-DS device

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• AEYE-DS Software

How Is the Trial Designed?

Group I: AEYE-DS Software Device Experimental Treatment 1 Intervention

An AI software device (AEYE-DS) to be used as a diagnostic tool to assist primary care clinicians in screening for diabetic retinopathy using digital funduscopic images. The device automatically detects more than mild diabetic retinopathy (mtmDR) in adults diagnosed with diabetes who have not been previously diagnosed with diabetic retinopathy

Who Is Running the Clinical Trial?

AEYE Health Inc

Lead Sponsor

Trials

3

Recruited

1,500+

AEYE Health LLC

Lead Sponsor

Trials

2

Recruited

890+

Published Research Related to This Trial

The MONA.health artificial intelligence screening software demonstrated high diagnostic performance for detecting diabetic retinopathy (DR) and diabetic macular edema (DME), achieving an area under the curve (AUC) of 97.28% for DR and 98.08% for DME on a private test set.

The software maintained strong sensitivity (90.91%) and specificity (94.24%) across various subgroups, although sensitivity was slightly lower for individuals over 65 years old and Caucasians, indicating its effectiveness across diverse populations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial Intelligence Software for Diabetic Eye Screening: Diagnostic Performance and Impact of Stratification. Peeters, F., Rommes, S., Elen, B., et al.[2023]

Citations

1. United States pubmed.ncbi.nlm.nih.gov

A Comparison of Artificial Intelligence and Human Diabetic Retinal Image Interpretation in an Urban Health System. [2022]

2. Switzerland pubmed.ncbi.nlm.nih.gov

Artificial Intelligence Software for Diabetic Eye Screening: Diagnostic Performance and Impact of Stratification. [2023]

3. England pubmed.ncbi.nlm.nih.gov

Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review. [2023]

4. United States pubmed.ncbi.nlm.nih.gov

Automated detection of diabetic retinopathy: results of a screening study. [2008]

5. Netherlands pubmed.ncbi.nlm.nih.gov

Clinical Decision Support for the Classification of Diabetic Retinopathy: A Comparison of Manual and Automated Results. [2017]

6. England pubmed.ncbi.nlm.nih.gov

The diagnostic accuracy of an intelligent and automated fundus disease image assessment system with lesion quantitative function (SmartEye) in diabetic patients. [2023]

7. United States pubmed.ncbi.nlm.nih.gov

Sensitivity and specificity of automated analysis of single-field non-mydriatic fundus photographs by Bosch DR Algorithm-Comparison with mydriatic fundus photography (ETDRS) for screening in undiagnosed diabetic retinopathy. [2018]

8. China pubmed.ncbi.nlm.nih.gov

Artificial intelligence on diabetic retinopathy diagnosis: an automatic classification method based on grey level co-occurrence matrix and naive Bayesian model. [2020]

9. England pubmed.ncbi.nlm.nih.gov

Cross-Camera External Validation for Artificial Intelligence Software in Diagnosis of Diabetic Retinopathy. [2022]