DiabetesNCT07151001
Pivotal Trial of Automated Artificial Intelligence (AI) Based System for Early Diagnosis of Diabetic Retinopathy
An observational study of Diabetes and Diabetic Retinopathy, run by iHealthScreen Inc at 1 site in United States.
Trial summary
In this pivotal trial, the investigators 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. Read more about this study
- Who is it for?
- Ages 22 years and olderHealthy volunteers can take part
- Conditions the study is for
- Diabetes, Diabetic Retinopathy
- Type of treatment
- Medical device
- How many people
- 935 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Jan 2025to 30 Jun 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 22 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Study subjects will be enrolled at primary care practices (internal medicine clinics, endocrinology clinics).
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Pivotal Trial of Automated AI-based System for Early Diagnosis of Diabetic Retinopathy Using Retinal Color Imaging
In this pivotal trial, the investigators 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.
Read the detailed description
In this pivotal trial, the investigators 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.
- Conditions
- Diabetes, Diabetic Retinopathy
- Being tested
- No intervention
- Keywords
- Early Diagnosis, Screening
- Medical subject headings
- Diabetes Mellitus, Diabetic Retinopathy, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionDevice
No intervention. Evaluate the automated DR screening software.
| Study group | Role | What they receive |
|---|---|---|
| One Group | One Cohort Receives: Device: No intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 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
Measured: 1-year or 2-year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Sep 2025
- Study start1 Jan 2025
- Primary completion9 Sep 2025
- Study completion30 Jun 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for DiabetesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 935 participants (actual)
Sponsor and investigators
- Lead sponsor
- iHealthScreen Inc
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- National Institutes of Health (NIH)
- Responsible party
- Sponsor
- Investigators
- Alauddin Bhuiyan, PhD, Principal investigator, iHealthScreen Inc
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07151001
- Sponsor's protocol ID
- 2024-02-iPredict-DR
- Status last verified
- Nov 2026
- First submitted
- 27 Aug 2025
- Last updated
- 6 Nov 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: In the future we will share the data.
Study at a glance
From the registry record, last updated by the sponsor on 6 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Pivotal Trial of Automated AI-based System for Early Diagnosis of Diabetic Retinopathy Using Retinal Color Imaging
- ClinicalTrials.gov ID
- NCT07151001
- Acronym / sponsor ID
- 2024-02-iPredict-DR
- Conditions
- Diabetes, Diabetic Retinopathy
- Keywords
- Early Diagnosis, Screening
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 935 participants (actual)
- Lead sponsor
- iHealthScreen IncIndustry (pharma, biotech, devices)
- Collaborators
- National Institutes of Health (NIH)
- Study start
- 1 Jan 2025 (actual)
- Primary completion
- 9 Sep 2025 (actual)
- Study completion
- 30 Jun 2026 (actual)
- First posted
- 2 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
In this pivotal trial, the investigators 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.
Detailed description
In this pivotal trial, the investigators 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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| One Group | – | Device: No interventionOne Cohort |
Who may qualify
- Ages
- 22 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Study subjects will be enrolled at primary care practices (internal medicine clinics, endocrinology clinics).
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (7 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No interventionNo intervention. Evaluate the automated DR screening software. | Device | One Group |
Primary outcome
mtmDR detected (Referable DR) OR mtmDR not detected (non-referable DR)
Time frame: 1-year or 2-year
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
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
Time frame: 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 for DR. Performance thresholds were defined at 80.0% for sensitivity and 80.0% for specificity
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Nov 2026.
- Individual participant dataSharing planned: Yes. In the future we will share the data.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Alauddin Bhuiyan, PhD principal investigator
iHealthScreen Inc
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetes and Diabetic Retinopathy, run by iHealthScreen Inc at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 6 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
mtmDR detected (Referable DR) OR mtmDR not detected (non-referable DR) (time frame: 1-year or 2-year). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Nov 2026.