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| Name | Class |
|---|---|
| iHealthScreen Inc | INDUSTRY |
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This study seeks to evaluate a system for the automated early detection of Age-Related Macular Degeneration (AMD). AMD is a condition in which there is breakdown of the macula of the eye, the part of the retina that is responsible for sharp, central vision. We will take pictures of subjects' eyes using an automated camera. These photographs will be securely transmitted and and then analyzed by a computer program which has been developed in other studies. The outcome of the computer program analysis will be compared with human analysis of these same pictures. If the computer analysis is has good enough accuracy, then this computer system could be used for wide-scale screening for AMD.
iPredict,an AI and telemedicine based software which used individual's color fundus image for early diagnosis of AMD and predict if an individual is at risk of progression to late AMD. iPredict platform integrates the server-side programs (the image analysis and deep-learning modules for AMD severity screening and prediction) and local remote computer/mobile devices (for collecting patient data and images). DRS plus camera will be used in the doctor's office. The remote devices will upload images and data to the server to analyze and screen AMD automatically. The telemedicine platform has been developed for web-based platform. The automatic analysis will be performed on the server, and a report will be sent to the patient/remote devices with an individual's AMD stage as referable or non-referable AMD, and a risk prediction score of developing late AMD (within a minute), and further recommendations to visit a nearby ophthalmologist.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| early/none vs. | For identification of early/none (i.e., non-referral level) Age Related Macular Degeneration (ARMD) |
| |
| intermediate/late AMD | intermediate/late (i.e., referral level) Age Related Macular Degeneration (ARMD) |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Referrable versus Non Referral AMD diagnostic test | Diagnostic Test | Artificial intelligence read reports Referrable versus Non Referral AMD |
|
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity of identification of referable and non-referable AMD for early diagnosis of AMD | Sensitivity of identification of referable and non-referable AMD for early diagnosis of AMD using the iPredict's AI-based AMD screening software utilizing color fundus imaging. | 2 years |
| Specificity of identification of referable and non-referable AMD for early diagnosis of AMD using the iPredict's AI-based AMD screening software utilizing color fundus imaging. | Using the gold standard (i.e., the ophthalmologist's grading), the sensitivity and specificity are calculated as: Sens=TP/(TP+FN) Spec=TN/(TN+FP) Where TP is the number of true positives (referable AMD subjects correctly classified), FN is the number of false negatives (referable AMD 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 AMD subjects incorrectly classified as referable AMD). | 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 | |
|---|---|---|---|---|
| Alauddin Bhuiyan, Ph.D. | Contact | 718 926 9000 | alauddin.bhuiyan@gmail.com | |
| Katy Tai | Contact | 2129794251 | ktai@nyee.edu |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| New York Eye and Ear Infirmary of Mount Sinai | Recruiting | New York | New York | 10003 | United States |
There is no IPD sharing plan at this time.
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| ID | Term |
|---|---|
| D008268 | Macular Degeneration |
| ID | Term |
|---|---|
| D012162 | Retinal Degeneration |
| D012164 | Retinal Diseases |
| D005128 | Eye Diseases |
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