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| Name | Class |
|---|---|
| iHealthScreen Inc | INDUSTRY |
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In this pivotal trial, we aim to perform a prospective study to find the efficacy of iPredict, an artificial intelligence (AI) based software tool on early diagnosis of Diabetic Retinopathy (DR)in the primary care, optometrist and other diabetes-screening 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 symptom 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.
In this pivotal trial, we aim to invite diabetic patients to participate in the trial by having non-dilated photos of their eyes taken by an FDA-approved DRS plus camera at their own doctor's office which will test the feasibility of our proposed automated AI based DR diagnosis software solution,. The color fundus photos will be captured and then be transmitted securely and analyzed by iHealthScreen's HIPAA compliant server at Amazon cloud. The deep learning module will analyze the image for finding the disease severity. The automated report will be generated which will report as referable DR or more than mild (mtm) DR detected i.e., moderate DR, severe DR - proliferative or non-proliferative DR or Non-referable DR or mtm DR not detected, i.e., mild DR or no DR.
The same images will be evaluated by 3 ophthalmologists and will be adjudicated if any disagreement between the gradings. The automatic and expert evaluation will be compared to compute the sensitivity, specificity and AUC.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| More than mild (mtm) Diabetic Retinopathy (DR) Not Detected or Non referable DR | More than mild Diabetic Retinopathy (mtm DR) not detected or non referable DR using the iPredict's AI-based DR screening software utilizing color fundus imaging. |
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| More than mild (mtm) Diabetic Retinopathy (DR) Detected or Referable DR | More than mild Diabetic Retinopathy (mtm DR), moderate to severe DR detected, non proliferative DR detected, proliferative DR detected or referable DR using the iPredict's AI-based DR screening software utilizing color fundus imaging. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Referable versus Non Referable Diabetic Retinopathy diagnostic test | Diagnostic Test | Artificial intelligence read reports Referable versus Non Referable Diabetic Retinopathy |
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| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity of identification of referable and non-referable Diabetic Retinopathy (DR) for early diagnosis of DR | iPredict DR can detect non-referable DR (normal retina or mild DR) and referable DR (moderate or severe DR including non-proliferative, proliferative DR and diabetic macular edema) at a similar level of expert ophthalmologists. The output of AI model and ophthalmologists' grading will be compared for image level and subject level accuracy measurement. Using the gold standard (i.e., the ophthalmologist's grading following ETDRS protocol), the sensitivity, specificity, precision, recall, accuracy, F-measure, positive predictive value and negative predictive value are calculated as: Sens=TP/(TP+FN) Spec=TN/(TN+FP) where TP is the number of true positives (referable DR subjects correctly classified), FN is the number of false negatives (referable DR subjects incorrectly classified as non-referable), TN is the number of true negatives (non-referable subjects correctly classified), and FP is the number of false positives (non-referable DR subjects incorrectly classified as referable DR). | 2 years |
| Specificity of identification of referable and non-referable Diabetic | iPredict DR can detect non-referable DR (normal retina or mild DR) and referable DR (moderate or severe DR including non-proliferative, proliferative DR and diabetic macular edema) at a similar level of expert ophthalmologists. The output of AI model and ophthalmologists' grading will be compared for image level and subject level accuracy measurement. | 2 years |
| Measure | Description | Time Frame |
|---|---|---|
| The accuracy of identification of referable and non-referable DR for early diagnosis of DR | 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/ophthalmologist for DR. Performance thresholds were defined at 85.0% for sensitivity and 82.5% for specificity. | 2 years |
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Inclusion Criteria:
Exclusion Criteria:
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Participants who fit the eligibility inclusion criteria and not the exclusion criteria.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| R. Theodore Smith, MD, PHD | Contact | 646-943-7925 | rolandtheodore.smith@mssm.edu |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| New York Eye and Ear Infirmary of Mount Sinai | New York | New York | 10003 | United States |
There is no IPD sharing plan at this time.
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| ID | Term |
|---|---|
| D003930 | Diabetic Retinopathy |
| ID | Term |
|---|---|
| D012164 | Retinal Diseases |
| D005128 | Eye Diseases |
| D003925 | Diabetic Angiopathies |
| D014652 | Vascular Diseases |
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| D002318 |
| Cardiovascular Diseases |
| D048909 | Diabetes Complications |
| D003920 | Diabetes Mellitus |
| D004700 | Endocrine System Diseases |