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.
Full Title of Study: “Pivotal Trial of an Automated AI-based System for Early Diagnosis and Prediction of Late Age Related Macular Degeneration in Primary Care Settings.”
- Study Type: Observational
- Study Design
- Time Perspective: Prospective
- Study Primary Completion Date: July 19, 2022
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.
- Diagnostic Test: Referrable versus Non Referral AMD diagnostic test
- Artificial intelligence read reports Referrable versus Non Referral AMD
Arms, Groups and Cohorts
- 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)
Clinical Trial Outcome Measures
- Sensitivity of identification of referable and non-referable AMD for early diagnosis of AMD
- Time Frame: 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.
- Time Frame: 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).
Participating in This Clinical Trial
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.
Gender Eligibility: All
Minimum Age: 50 Years
Maximum Age: N/A
Are Healthy Volunteers Accepted: Accepts Healthy Volunteers
- Lead Sponsor
- The New York Eye & Ear Infirmary
- iHealthScreen Inc
- Provider of Information About this Clinical Study
- Overall Contact(s)
- Alauddin Bhuiyan, Ph.D., 718 926 9000, email@example.com
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