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

Pivotal Trial of Automated AI-based System for Early Diagnosis of Diabetic Retinopathy Using Retinal Color Imaging

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.

Study Overview

• Status: Recruiting
• Conditions: Diabetes; Diabetic Retinopathy
• Intervention / Treatment: Device: No intervention

Detailed Description

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.

American Academy of Ophthalmology has suggested a 5-level DR disease severity scale (No DR, Mild DR, Moderate DR, Severe DR or Proliferative DR) based on the abnormalities in the retina such as microaneurysms, exudates, hemorrhages, intraretinal microvascular abnormalities (IRMA), and neovascularization. Automated screening for Diabetic Retinopathy has a potential to identify people at risk of developing sight-threatening disease and save millions of dollars in healthcare costs. To accomplish this, it is crucial to perform large scale population screening to identify the individuals with mild or early DR and better predict those at risk of developing late stage DR. A system that takes advantage of telemedicine with automated DR screening in reaching the mass populations in both urban and rural areas with the patient convenience is currently not widely available.

Considering this urgent need, iHealthScreen has developed an automated software tool for DR screening which is based on artificial intelligence (AI) and make it widely available in both urban and remote/rural areas and for large-scale screening through a telemedicine platform, and thereby have the potential to prevent blindness in diabetic patients.

• Study Type: Observational
• Enrollment (Estimated): 922

Contacts and Locations

• Study Contact: Name: Alauddin Bhuiyan; Phone Number: 718-926-9000; Email: bhuiyan@ihealthscreen.org
• Study Contact Backup: Name: Fariha Nuha, BS (Comp. Biology); Phone Number: 718-912-0837; Email: fariha@ihealthscreen.org

Study Locations

• United States
  • New York
    • Richmond Hill, New York, United States, 11418
      • Recruiting
      • iHealthScreen Inc.
      • Principal Investigator: Alauddin Bhuiyan

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

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

Description

Inclusion Criteria:

• 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.

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
One Group; One Cohort	Device: No intervention; No intervention. Evaluate the automated DR screening software.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: iHealthScreen Inc
• Collaborators: National Institutes of Health (NIH)
• Investigators: Principal Investigator: Alauddin Bhuiyan, PhD, iHealthScreen Inc

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2025
• Primary Completion (Estimated): July 31, 2026
• Study Completion (Estimated): July 31, 2027

Study Registration Dates

• First Submitted: August 27, 2025
• First Submitted That Met QC Criteria: August 27, 2025
• First Posted (Actual): September 2, 2025

Study Record Updates

• Last Update Posted (Actual): September 2, 2025
• Last Update Submitted That Met QC Criteria: August 27, 2025
• Last Verified: August 1, 2025

More Information

Terms related to this study

• Keywords: Screening; Early Diagnosis
• Additional Relevant MeSH Terms: Endocrine System Diseases; Vascular Diseases; Cardiovascular Diseases; Pathologic Processes; Metabolic Diseases; Glucose Metabolism Disorders; Eye Diseases; Diabetic Angiopathies; Diabetes Complications; Retinal Diseases; Pathological Conditions, Signs and Symptoms; Nutritional and Metabolic Diseases; Diabetic Retinopathy; Disease; Diabetes Mellitus
• Other Study ID Numbers: 2024-02-iPredict-DR

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: In the future we will share the data.
• IPD Sharing Time Frame: after 5 years
• IPD Sharing Access Criteria: To be enrolled in the NIH data sharing portal - dbGAP.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No