AI-Based Screening for Glaucoma

(AI-RONA Trial)

Overseen By Dawn Matthies, PhD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Alabama at Birmingham

Disqualifiers: Declines consent, Non-English speakers

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

Compensation: Varies

Number of Visits: 3

Duration: 2-3 days

1800 Participants Needed

Trial Summary

What is the purpose of this trial?

The goal of this clinical trial is to learn if implementation of an eye screening program at Federally Qualified Health Center (FQHC) clinics provides results that participants may have glaucoma, and/or other eye conditions (diabetic retinopathy, cataract, visual acuity impairment). The glaucoma screening will incorporate use of an artificial intelligence (AI)-assisted screening tool. This project is called AI-RONA. The main questions it aims to answer are: * How does this eye screening program compare to the rate of glaucoma and other eye conditions detected at other FQHC clinics where the screening program has not been implemented? * Do particpants who screen positive for these conditions adhere to the physician's recommendation for a follow-up examination by an optometrist or ophthalmologist? * Are referral rates for a follow-up comprehensive eye exam by an optometrist or ophthalmologist similar to those implemented by an ophthalmologist using telemedicine (that is, using the results of the screening to make a diagnosis remotely)? * What is the cost-effectiveness of the AI-assisted screening program in diagnosing glaucoma as compared to a physician-guided program? * Are participants completing the screening satisfied with it? * Are physicians at the FQHC clinics administering the screening satisfied with it? Participants will: * Undergo an ocular screening whose goal is to detect glaucoma, diabetic retinopathy, cataract, and/or impairment in visual acuity. If the screening indicates that participants may have these conditions, participants will be referred for a comprehensive eye examination by an optometrist or ophthalmologist. * Following the screening, participants and physicians will complete a survey on their satisfaction with the program.

Will I have to stop taking my current medications?

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

What data supports the effectiveness of the AI-based Glaucoma Screening treatment?

Research shows that AI, particularly deep learning, can outperform eye doctors in detecting glaucoma by analyzing eye images, which suggests it could be a powerful tool for early diagnosis and monitoring of the disease. ^{1 2 3 4 5}

Is AI-based glaucoma screening safe for humans?

The research does not provide specific safety data for AI-based glaucoma screening, but it focuses on improving diagnosis and management of glaucoma using AI, which suggests it is generally considered safe for use in clinical settings. ^{2 3 4 6 7}

How does AI-based Glaucoma Screening differ from other treatments for glaucoma?

AI-based Glaucoma Screening is unique because it uses artificial intelligence to analyze eye images and detect glaucoma early, often outperforming human experts. This approach is non-invasive, cost-effective, and can handle large datasets, making it more accessible and efficient compared to traditional methods that rely heavily on human interpretation. ^{1 4 5 6 8}

Research Team

Cynthia Owsley, PhD

Principal Investigator

University of Alabama at Birmingham

Eligibility Criteria

This trial is for individuals at clinics where they might have glaucoma or other eye conditions like diabetic retinopathy and cataracts. It's designed to see if AI can help screen these issues effectively. Participants will be screened and possibly referred for further examination.

Inclusion Criteria

I am 18 or older with type 1 or type 2 diabetes.

I am African American or Hispanic and 40 years old or older.

I am over 18 and have a family history of glaucoma.

Exclusion Criteria

I am unable to communicate in English.

I choose not to sign the consent form.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-3 days

1 visit (in-person)

Treatment

Participants undergo an ocular screening using AI-assisted tools to detect glaucoma, diabetic retinopathy, cataract, and visual acuity impairment

2-3 days

1 visit (in-person)

Follow-up

Participants diagnosed with conditions are referred for a comprehensive eye examination by an optometrist or ophthalmologist

up to 4 months

1 visit (in-person)

Satisfaction Survey

Participants and physicians complete a survey on their satisfaction with the screening program

1 day

Treatment Details

Interventions

• AI-based Glaucoma Screening

Trial Overview The study tests an AI-assisted screening tool called AI-RONA for detecting glaucoma and other eye conditions in health centers. It compares detection rates, follow-up adherence, referral rates, cost-effectiveness, and satisfaction with traditional methods.

Participant Groups

Group I: The Federally Qualified Health Centers (FQHCs) Experimental Treatment 1 Intervention

Eight FQHCs will participate in this project. Four FQHCs will implement a glaucoma screening protocol and a protocol for detecting diabetic retinopathy, cataract, and visual acuity impairment, while the other four will be standard of care without the above screening protocol.

Group II: Primary Standard of Care Active Control 1 Intervention

Primary care provider asks participants if they are having symptoms or problems with their eyes or vision.

Who Is Running the Clinical Trial?

University of Alabama at Birmingham

Lead Sponsor

Trials

1,677

Recruited

2,458,000+

Centers for Disease Control and Prevention

Collaborator

Trials

902

Recruited

25,020,000+

University of California, San Diego

Collaborator

Trials

1,215

Recruited

1,593,000+

References

1. Switzerland pubmed.ncbi.nlm.nih.gov

Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions. [2023]

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

Artificial intelligence for glaucoma: state of the art and future perspectives. [2023]

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

Applications of deep learning in detection of glaucoma: A systematic review. [2021]

4. England pubmed.ncbi.nlm.nih.gov

The impact of artificial intelligence in the diagnosis and management of glaucoma. [2023]

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

Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning. [2021]

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

AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge. [2023]

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

Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images. [2022]

8. Romania pubmed.ncbi.nlm.nih.gov

Advancing glaucoma detection with convolutional neural networks: a paradigm shift in ophthalmology. [2023]