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Pivotal Trial of an Automated AI-based System for Early Diagnosis and Prediction of Late Age-related Macular Degeneration

Pivotal Trial of an Automated AI-based System for Early Diagnosis and Prediction of Late Age-related Macular Degeneration in Ophthalmology and Primary Care Settings

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT07084883
Enrollment
1076
Registered
2025-07-25
Start date
2024-08-01
Completion date
2027-07-31
Last updated
2025-08-28

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Age-related Macular Degeneration (AMD)

Keywords

Screening, Prediction, age-related macular degeneration, AMD

Brief summary

The purpose of this study is to perform a pivotal trial of iPredict, an automated AI-based system for early diagnosis and prediction of late AMD in primary care and ophthalmology settings. Patients will be invited to participate in this study by having non-dilated photos of their eyes taken by an FDA approved fundus camera (DRSPlus from Centervue Inc., CA), at their primary care doctor's office or general ophthalmologist office. The photos will then be transmitted securely and analyzed by computer in the cloud (telemedicine features). Sufficient accuracy of the automatic system has been established compared to the ophthalmologist's diagnosis. In this study, we aim to validate the system against the prospectively taken OCT image and color fundus images.

Detailed description

The purpose of this study is to perform a pivotal trial of iPredict, an automated AI-based system for early diagnosis and prediction of late AMD in primary care and ophthalmology settings. Patients will be invited to participate in this study by having non-dilated photos of their eyes taken by an FDA approved fundus camera (DRSPlus from Centervue Inc., CA), at their primary care doctor's office or general ophthalmologist office. The photos will then be transmitted securely and analyzed by computer in the cloud (telemedicine features). Sufficient accuracy of the automatic system has been established compared to the ophthalmologist's diagnosis. In this study, we aim to validate the system against the prospectively taken OCT image and color fundus images. Background AMD affects 15 million Americans, with 200,000 new cases diagnosed each year. At present, there is no treatment for dry AMD. Besides blindness, AMD has other indirect complications such as depression, social dependency, and the risk of fall and injury. The prevalence of this disease is expected to grow substantially as life expectancy continues to increase and record numbers of Baby Boomers enter their senior years. The total direct cost of AMD is $220 billion per year and is expected to increase \ 1.5 fold. The Age-Related Eye Disease Study (AREDS) showed that specific vitamin supplementation protocols can reduce the risk of progression from intermediate to late AMD by \ 25% which in turn could lower the cost of AMD 17.6% if fully implemented. To accomplish this, it is crucial to perform large scale population screening to identify the individuals with early- or intermediate-stage of AMD and better predict those at risk of developing late AMD, but such a system is currently not available. Although articles have been published on automatic AMD pathology detection, none of these systems are available for screening due to lack of validation and commercial readiness. Considering this urgent need, we aim to develop an automated tool iPredict for early diagnosis and prediction of AMD, and make it widely available in both urban and remote/rural areas and for large- scale screening (through its telemedicine features), and thereby prevent blindness. Primary and Secondary Study Endpoints The accuracy of the iPredict software developed by iHealthScreen system in early diagnosis of AMD using color retinal photos vs. that of human expert graders for AMD. Also, the prediction of late AMD progression in 1 or two years.

Interventions

DEVICENo intervention.

No intervention. Evaluate the automated AMD screening software.

Sponsors

National Institutes of Health (NIH)
CollaboratorNIH
National Eye Institute (NEI)
CollaboratorNIH
iHealthScreen Inc
Lead SponsorINDUSTRY

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
50 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

* Subjects will be recruited if willing and able to comply with clinic visit and study-related procedures, and provide signed informed consent

Exclusion criteria

* Already diagnosed with AMD, unable to provide informed consent and currently under treatment of retinal disease.

Design outcomes

Primary

MeasureTime frameDescription
Referable and non-referable AMD. And Predict the Incidence of AMD in 1 year or 2-year.1 year or 2-yearReferable and non-referable AMD. And Predict the Incidence of AMD in 1 year or 2-year.

Countries

United States

Contacts

Primary ContactAlauddin Bhuiyan
bhuiyan@ihealthscreen.org718-926-9000
Backup ContactFariha Nuha, BS (Comp. Biology)
fariha@ihealthscreen.org718-912-0837

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026