Pivotal Trial of Automated Artificial Intelligence (AI) Based System for Early Diagnosis of Diabetic Retinopathy

NCT07151001 | Recruiting DMC

• 1 other identifier: 2024-02-iPredict-DR
• Study Type: observational
• Target: 922
• Locations: 1 country
• Sites: 1

Brief Summary

In this pivotal trial, we aim to perform a prospective study to find the efficacy of iPredict-DR, an artificial intelligence (AI) based software tool on early diagnosis of Diabetic Retinopathy (DR) in the primary care and endocrinology clinics. DR is one of the leading causes of blindness in the United States and other developed countries. Every individual with diabetes is at risk of DR. It does not show any symptoms until the disease is progressed to advanced stages. If the disease is caught at an early stage, it can be prevented, managed, or treated effectively. Currently, screening for DR is done by the Ophthalmologists, which is limited to areas with limited availability. This is also time-consuming and expensive. All of these can be complemented by automated screening and set up the screening in the primary care clinics.

Conditions

Diabetes; Diabetic Retinopathy

Keywords

Early Diagnosis; Screening

Outcome Measures

Primary Outcomes (2)

• mtmDR detected (Referable DR) OR mtmDR not detected (non-referable DR)

  Sensitivity and specificity of identification of referable and non-referable DR for early diagnosis of DR using the iPredict-DR's AI-based DR screening software utilizing color fundus imaging. iPredict-DR can detect non-referable DR (normal retina or mild DR) and referable DR (moderate or severe DR including moderate non-proliferative, proliferative DR and diabetic macular edema) at a similar level of expert ophthalmologists. For this, the healthcare workers will be taking the disc and macula center 45-degree field view images using DRSPlus camera (from iCare Inc.). The output of AI model and ground truth (produced by graders from reading centers) will be compared for image level and subject level accuracy measurements. The worst eye will be considered to define a subject's referability or non-referability to an ophthalmologist. Using the ground truth/gold standard, the sensitivity, specificity, precision, recall, accuracy, F-measure, positive predictive value and negative predictive

  1-year or 2-year

• The accuracy of the iPredict-DR software developed by iHealthScreen system in early diagnosis of DR using color retinal photos vs. that of human expert graders

  The accuracy of the iPredict-DR software developed by iHealthScreen system in early diagnosis of DR using color retinal photos vs. that of human expert graders for DR. Performance thresholds were defined at 80.0% for sensitivity and 80.0% for specificity

  1-year or 2-year

Study Arms (1)

One Group

One Cohort

Device: No intervention

Interventions

No intervention DEVICE

No intervention. Evaluate the automated DR screening software.

One Group

Eligibility Criteria

• Age: 22 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Study subjects will be enrolled at primary care practices (internal medicine clinics, endocrinology clinics).

You may qualify if:

• Age of Subjects: Patients ≥ 22 years of age.
• Gender of Subjects: Both males and females will be invited to participate.
• Subjects with diabetes (A1C level ≥ 6.5).
• Subjects must be willing and are able to comply with clinic visit, understand the study-related procedures/provisions, and provide signed informed consent.

You may not qualify if:

• Unable to understand the study, Our unable to or unwilling to sign the informed consent
• Previously diagnosed with macular edema, any form of diabetic retinopathy, radiation retinopathy, or retinal vein occlusion
• participants who are experiencing persistent vision loss, blurred vision, or other vision problems that should be evaluated by an eye care provider
• subjects whose retinal images were used in training, validating, or developing the device
• Currently participating in another investigational eye study or actively receiving investigational product for DR or DME.
• A condition that, in the opinion of the investigator, would preclude participation in the study;
• Contraindicated for imaging by fundus imaging systems used in the study because of hypersensitivity to light, recently underwent photodynamic therapy, or was taking medication that causes photosensitivity.

Sponsors & Collaborators

• iHealthScreen Inc: lead
• National Institutes of Health (NIH): collaborator

Study Sites (1)

iHealthScreen Inc.

Richmond Hill, New York, 11418, United States

RECRUITING

MeSH Terms

Conditions

Diabetes Mellitus; Diabetic Retinopathy; Disease

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Retinal Diseases; Eye Diseases; Diabetic Angiopathies; Vascular Diseases; Cardiovascular Diseases; Diabetes Complications; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Alauddin Bhuiyan, PhD

  iHealthScreen Inc

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Alauddin Bhuiyan

CONTACT

718-926-9000; bhuiyan@ihealthscreen.org

Fariha Nuha, BS (Comp. Biology)

CONTACT

718-912-0837; fariha@ihealthscreen.org

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 27, 2025
• First Posted: September 2, 2025
• Study Start: January 1, 2025
• Primary Completion (Estimated): July 31, 2026
• Study Completion (Estimated): July 31, 2027
• Last Updated: September 2, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR
• Time Frame: after 5 years
• Access Criteria: To be enrolled in the NIH data sharing portal - dbGAP.

Locations

US (1)