500 Participants Needed

AI-Powered Eye Exam for Diabetes

(ACCESS2 Trial)

RM
AL
Overseen ByAlvin Liu, MD
Age: < 65
Sex: Any
Trial Phase: Academic
Sponsor: Johns Hopkins University
No Placebo GroupAll trial participants will receive the active study treatment (no placebo)
Approved in 5 JurisdictionsThis treatment is already approved in other countries

Trial Summary

What is the purpose of this trial?

The purpose of this study is to determine if use of a nonmydriatic fundus camera using autonomous artificial intelligence software at the point of care increases the proportion of underserved youth with diabetes screened for diabetic retinopathy.

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 data supports the effectiveness of the AI-Powered Eye Exam for Diabetes treatment?

The AI system for diabetic retinopathy screening has shown high sensitivity (87.2%) and specificity (90.7%) in detecting more than mild diabetic retinopathy and diabetic macular edema, making it effective in primary care settings and authorized by the FDA to help prevent vision loss in people with diabetes.12345

Is the AI-powered eye exam for diabetes safe for humans?

The AI-powered eye exams, such as IDx-DR and EyeArt, have been studied extensively and are considered safe for use in humans. These systems have been evaluated in real-world settings and have shown to be effective in screening for diabetic retinopathy without any reported safety concerns.15678

How does the AI-Powered Eye Exam for Diabetes differ from other treatments for diabetic retinopathy?

The AI-Powered Eye Exam for Diabetes is unique because it uses artificial intelligence to autonomously detect diabetic retinopathy from eye images, offering a high sensitivity rate. Unlike traditional methods that rely solely on human interpretation, this AI system can quickly analyze large volumes of images, potentially predicting disease progression and reducing the burden on eye care professionals.12359

Research Team

RM

Risa M Wolf, MD

Principal Investigator

Johns Hopkins University

Eligibility Criteria

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

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

Treatment Details

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.
Participant Groups
1Treatment groups
Experimental Treatment
Group I: Diabetic Retinopathy Exam at the point of careExperimental Treatment1 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.

Find a Clinic Near You

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+

Findings from Research

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.
The Real-World Impact of Artificial Intelligence on Diabetic Retinopathy Screening in Primary Care.Cuadros, J.[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.
Artificial Intelligence in the assessment of diabetic retinopathy from fundus photographs.Gilbert, MJ., Sun, JK.[2021]
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.
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]

References

The Real-World Impact of Artificial Intelligence on Diabetic Retinopathy Screening in Primary Care. [2021]
Artificial Intelligence in the assessment of diabetic retinopathy from fundus photographs. [2021]
Use in clinical practice of an automated screening method of diabetic retinopathy that can be derived using a diagnostic artificial intelligence system. [2021]
Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. [2020]
AI-Human Hybrid Workflow Enhances Teleophthalmology for the Detection of Diabetic Retinopathy. [2023]
The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes. [2020]
The SEE Study: Safety, Efficacy, and Equity of Implementing Autonomous Artificial Intelligence for Diagnosing Diabetic Retinopathy in Youth. [2021]
Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting. [2023]
Diabetic retinopathy classification for supervised machine learning algorithms. [2022]
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