Age Related Macular Degeneration
Conditions
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTReferrable versus Non Referral AMD diagnostic test
Artificial intelligence read reports Referrable versus Non Referral AMD
Sponsors
iHealthScreen Inc
The New York Eye & Ear Infirmary
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
50 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Subjects will be recruited if willing and able to comply with clinic visit and study-related procedures, and provide signed informed consent 2. Gender of Subjects: Both males and females will be invited to participate. 3. Age of Subjects: Patients will be over 50 years and older
Exclusion criteria
1. Unable to provide informed consent. 2. Other retinal degenerations and retinal vascular diseases such as diabetic retinopathy or macular edema, prior retinal surgery.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity of identification of referable and non-referable AMD for early diagnosis of AMD | 2 years | 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. |
| 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. | 2 years | 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). |
Countries
United States
Contacts
Primary ContactAlauddin Bhuiyan, Ph.D.
Backup ContactKaty Tai
Outcome results
None listed