AI-Powered Eye Exam for Diabetes

(ACCESS2 Trial)

Overseen By Alvin Liu, MD

Age: < 65

Sex: Any

Trial Phase: Academic

Sponsor: Johns Hopkins University

Disqualifiers: Recent diabetic eye exam

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

Approved in 5 Jurisdictions This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 1

500 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests an AI-powered eye exam to determine if it increases screening rates for diabetic retinopathy among young people with diabetes. The exam, called the Point of Care Autonomous AI diabetic retinopathy exam, uses a special camera and AI software to quickly detect signs of this eye issue at the doctor's office. If the AI exam identifies problems, the patient receives a referral for a more detailed eye exam. The trial seeks young participants who have had Type 1 diabetes for at least three years and are either 11 years old or going through puberty, or who have Type 2 diabetes. As an unphased trial, this study provides a unique opportunity to contribute to innovative research that could enhance early detection of diabetic retinopathy in young people.

Do I need to stop my current medications for the trial?

The trial information does not specify whether you need to stop taking your current medications.

What prior data suggests that this AI-powered eye exam is safe for diabetic retinopathy screening?

Research has shown that autonomous AI for diabetic retinopathy exams is safe. The EyeArt system, an AI tool for these exams, has received FDA approval, meeting strict safety standards. Studies indicate it performs well in regular healthcare settings. It boasts a high accuracy rate, correctly identifying 96% of individuals with the disease (sensitivity) and 88% of those without it (specificity). No major safety issues have emerged in these studies, suggesting the AI system is well-tolerated and poses little risk to participants. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the AI-powered eye exam for diabetic retinopathy because it offers an innovative approach to early detection. Unlike traditional methods that require a specialist to evaluate retinal images, this system uses autonomous AI to analyze the images on the spot. This means quicker diagnoses and potentially faster referrals for necessary care. It streamlines the process, making eye exams more accessible and efficient for people with diabetes, which is crucial in preventing vision loss.

What evidence suggests that this AI-powered eye exam is effective for diabetic retinopathy screening?

Research has shown that independent AI for eye exams in people with diabetes can effectively detect eye problems. In this trial, participants will undergo a point-of-care diabetic retinopathy eye exam using autonomous AI. Studies indicate that these AI tools accurately detect signs of diabetic retinopathy, a common eye issue in diabetes. In primary care settings, this technology has increased the number of completed diabetic eye exams. Early detection of eye issues is crucial to prevent serious vision problems. Overall, evidence suggests that AI in eye exams can make screenings more efficient and accessible for underserved young people with diabetes. ^{1 2 3 6 7}

Who Is on the Research Team?

Risa M Wolf, MD

Principal Investigator

Johns Hopkins University

Are You a Good Fit for This Trial?

This trial is for young people with Type 1 diabetes for at least 3 years, aged 11 or older and in puberty, or those diagnosed with Type 2 diabetes. It's aimed at helping underserved youth who haven't had a diabetic eye exam in the past year.

Inclusion Criteria

I have had Type 1 diabetes for 3+ years and am at least 11 years old or in puberty.

I have been diagnosed with Type 2 diabetes.

Exclusion Criteria

You had an eye exam for diabetes in the past year.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Diabetic Retinopathy Exam

Participants undergo a point-of-care diabetic retinopathy eye exam using autonomous AI. Immediate results are provided, and those with abnormal results are referred for a dilated eye exam.

1 day

1 visit (in-person)

Follow-up

Participants are monitored for safety and effectiveness after the initial exam, with a focus on agreement in interpretation of retinal images over time.

2 years

What Are the Treatments Tested in This Trial?

Interventions

• Point of Care Autonomous AI diabetic retinopathy exam

Trial Overview The study tests if an AI-powered camera can help screen more kids with diabetes for eye problems caused by their condition (diabetic retinopathy) when used during regular care visits.

How Is the Trial Designed?

Group I: Diabetic Retinopathy Exam at the point of care Experimental Treatment 1 Intervention

Participants will undergo a point of care diabetic retinopathy eye exam using autonomous AI. Those that test positive will be referred to Eye Care Provider for dilated eye exam.

Who Is Running the Clinical Trial?

Johns Hopkins University

Lead Sponsor

Trials

2,366

Recruited

15,160,000+

Juvenile Diabetes Research Foundation

Collaborator

Trials

237

Recruited

142,000+

National Eye Institute (NEI)

Collaborator

Trials

572

Recruited

1,320,000+

Published Research Related to This Trial

Artificial intelligence (AI) technology can significantly enhance the diagnosis and treatment of diabetic retinopathy, a leading cause of preventable blindness, by providing early detection and monitoring capabilities.

The effectiveness of AI in this field is hindered by inconsistent labeling standards in retinal exam datasets, highlighting the need for standardized classification methods to improve the reliability of AI algorithms.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Diabetic retinopathy classification for supervised machine learning algorithms. Nakayama, LF., Ribeiro, LZ., Gonçalves, MB., et al.[2022]

The IDx autonomous diabetic retinopathy screening program demonstrated a perfect sensitivity of 100% in detecting referable diabetic retinopathy, but had a lower specificity of 82%, leading to a high rate of unnecessary referrals.

With a positive predictive value of only 19%, the program may overwhelm ophthalmologists and primary care clinics, suggesting a need for improved AI systems that can provide better specificity and detailed lesion annotations to enhance patient management and treatment adherence.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Real-World Impact of Artificial Intelligence on Diabetic Retinopathy Screening in Primary Care. Cuadros, J.[2021]

In a pivotal trial involving 900 participants, an AI system demonstrated high sensitivity (87.2%) and specificity (90.7%) in detecting diabetic retinopathy, surpassing pre-specified performance benchmarks.

The FDA has authorized this AI diagnostic system for use in primary care settings, marking it as the first autonomous AI diagnostic tool approved in medicine, which could significantly enhance early detection and prevention of vision loss in diabetic patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. Abràmoff, MD., Lavin, PT., Birch, M., et al.[2020]

The EyeArt system, an automated AI technology, demonstrated high diagnostic efficacy for detecting referral-warranted diabetic retinopathy (DR) with a sensitivity of 91.3% and specificity of 91.1% across 850,908 fundus images from 101,710 patient visits.

For encounters with potentially treatable DR, the EyeArt system achieved an impressive sensitivity of 98.5%, indicating its effectiveness in identifying patients who need urgent care, making it a valuable tool for healthcare providers in managing diabetic eye health.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes. Bhaskaranand, M., Ramachandra, C., Bhat, S., et al.[2020]

The AI-based diagnostic tool IDx-DR demonstrated high sensitivity (95.5%) for detecting more-than-mild diabetic retinopathy (MTMDR), but had lower specificity (60.3%) compared to human retina specialists, who had a sensitivity of 69.5% and a much higher specificity of 96.9%.

A hybrid workflow combining AI assessment with human expert review significantly improved diagnostic accuracy, achieving a sensitivity of 95.5% and a specificity of 98.2%, while also enhancing the gradability of fundus photographs from 62.5% to 95.6%.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

AI-Human Hybrid Workflow Enhances Teleophthalmology for the Detection of Diabetic Retinopathy. Dow, ER., Khan, NC., Chen, KM., et al.[2023]

In a study of 310 youth with diabetes, an autonomous AI system demonstrated a high diagnosability rate of 97.5% for detecting diabetic retinopathy (DR), with a sensitivity of 85.7% and specificity of 79.3% compared to retinal specialists' assessments.

The implementation of AI for diabetic eye exams significantly improved adherence to screening guidelines from 49% to 95%, highlighting its potential to enhance early detection of DR in young patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The SEE Study: Safety, Efficacy, and Equity of Implementing Autonomous Artificial Intelligence for Diagnosing Diabetic Retinopathy in Youth. Wolf, RM., Liu, TYA., Thomas, C., et al.[2021]

The AI algorithm Selena+ in the handheld Optomed Aurora fundus camera demonstrated a high sensitivity and specificity of 96.8% for detecting diabetic retinopathy (DR) in a study of 256 patients, making it a reliable screening tool.

The study found a perfect agreement (100%) between the AI analysis and the expert retina specialist's evaluation, suggesting that this technology could effectively support large-scale screening efforts for DR in real-world clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting. Lupidi, M., Danieli, L., Fruttini, D., et al.[2023]

The autonomous diagnostic AI system demonstrated 100% sensitivity for detecting derivable diabetic retinopathy (RDR) and diabetic retinopathy with decreased vision (VTDR), indicating it can accurately identify all cases of these conditions.

The AI system also showed high specificity (81.82% for RDR and 94.64% for VTDR), suggesting it can effectively distinguish between affected and non-affected individuals, which could enhance accessibility to screening in primary care settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Use in clinical practice of an automated screening method of diabetic retinopathy that can be derived using a diagnostic artificial intelligence system. Peris-Martínez, C., Shaha, A., Clarida, W., et al.[2021]

The global prevalence of diabetes is projected to reach 700 million individuals in the next 25 years, increasing the risk of vision loss from diabetic eye disease and highlighting the need for innovative detection tools.

Artificial intelligence (AI) has shown great promise in detecting diabetic retinopathy (DR) by analyzing fundus photographs, using machine learning to grade DR and potentially predict its progression, which could help alleviate the burden on eye care professionals.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial Intelligence in the assessment of diabetic retinopathy from fundus photographs. Gilbert, MJ., Sun, JK.[2021]

Citations

1. ophthalmologyscience.org ophthalmologyscience.org/article/S2666-9145(25)00233-7/fulltext

Autonomous Artificial Intelligence in Diabetic Retinopathy ... The literature review found 6 publications reporting diagnostic accuracy data of autonomous AI DR testing in primary care office settings, ...

2. clinicaltrials.gov clinicaltrials.gov/study/NCT06721351

Diabetic Retinopathy Screening Point-of-Care Artificial ... This research study is being conducted to improve eye care by using artificial intelligence (AI) to make diabetic eye screenings faster and more accessible.

3. sciencedirect.com sciencedirect.com/science/article/pii/S2666914525002337

Autonomous Artificial Intelligence in Diabetic Retinopathy ... The literature review found 6 publications reporting diagnostic accuracy data of autonomous AI DR testing in primary care office settings, ...

4. nature.com nature.com/articles/s41467-023-44676-z

Autonomous artificial intelligence increases screening and ... We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates.

5. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC8120059/

Autonomous Artificial Intelligence in Diabetic Retinopathy Our article focuses on the use of autonomous AI algorithms (ie, algorithms that can make clinical decisions without human oversight) in diagnostic imaging.

6. jamanetwork.com jamanetwork.com/journals/jamanetworkopen/fullarticle/2840380

Diabetic Retinopathy Screening Among Federally Qualified ... Findings The Diabetic Retinopathy Screening Point-of-Care Artificial Intelligence trial aims to demonstrate that a multicomponent approach—AI- ...

7. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC9636573/

Artificial Intelligence Detection of Diabetic Retinopathy The EyeArt system (Eyenuk, Inc) is also an FDA-cleared AI-based system that can enable point-of-care screening with 96% sensitivity, 88% specificity, and 97% ...