Pivotal Trial of Automated Artificial Intelligence (AI) Based System for Early Diagnosis of Diabetic Retinopathy
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age22+
SponsoriHealthScreen Inc
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
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.
Disqualifiers
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
Trial population
Study subjects will be enrolled at primary care practices (internal medicine clinics, endocrinology clinics).
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
No intervention
DeviceNo intervention. Evaluate the automated DR screening software.
Treatment groups
922 Participants
are divided into 1 treatment groupGroup A: One Group1 intervention
Trial outcomes
Primary outcomes
1
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
Time frame
1-year or 2-year2
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
Time frame
1-year or 2-yearSponsors and contacts
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iHealthScreen Inc
Lead sponsor
National Institutes of Health (NIH)
Collaborator
Source ClinicalTrials.gov