Telemedicine in Age-Related Macular Degeneration

Pivotal Trial of an Automated AI-based System for Early Diagnosis and Prediction of Late Age Related Macular Degeneration in Primary Care Settings.

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.

Study Overview

• Status: Recruiting
• Conditions: Age Related Macular Degeneration
• Intervention / Treatment: Diagnostic test: Referrable versus Non Referral AMD diagnostic test

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.

• Study Type: Observational
• Enrollment (Anticipated): 1000

Contacts and Locations

• Study Contact: Name: Alauddin Bhuiyan, Ph.D.; Phone Number: 718 926 9000; Email: alauddin.bhuiyan@gmail.com
• Study Contact Backup: Name: Katy Tai; Phone Number: 2129794251; Email: ktai@nyee.edu

Study Locations

• United States
  • New York
    • New York, New York, United States, 10003
      • Recruiting
      • New York Eye and Ear Infirmary of Mount Sinai
      • Contact: Katy Tai; Phone Number: 212-979-4251; Email: ktai@nyee.edu
      • Principal Investigator: R. Theodore Smith, MD, PHD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 50 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Participants who fit the eligibility inclusion criteria and not the exclusion criteria.

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
early/none vs.; For identification of early/none (i.e., non-referral level) Age Related Macular Degeneration (ARMD)	Diagnostic test: Referrable versus Non Referral AMD diagnostic test; Artificial intelligence read reports Referrable versus Non Referral AMD
intermediate/late AMD; intermediate/late (i.e., referral level) Age Related Macular Degeneration (ARMD)	Diagnostic test: Referrable versus Non Referral AMD diagnostic test; Artificial intelligence read reports Referrable versus Non Referral AMD

What is the study measuring?

Primary Outcome Measures

Outcome Measure	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

Collaborators and Investigators

• Sponsor: The New York Eye & Ear Infirmary
• Collaborators: iHealthScreen Inc

Study record dates

Study Major Dates

• Study Start (Actual): July 19, 2020
• Primary Completion (Anticipated): July 19, 2022
• Study Completion (Anticipated): December 31, 2022

Study Registration Dates

• First Submitted: April 19, 2021
• First Submitted That Met QC Criteria: April 26, 2021
• First Posted (Actual): April 28, 2021

Study Record Updates

• Last Update Posted (Actual): April 28, 2021
• Last Update Submitted That Met QC Criteria: April 26, 2021
• Last Verified: April 1, 2021

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Eye Diseases; Retinal Degeneration; Retinal Diseases; Macular Degeneration
• Other Study ID Numbers: 18-00787

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No
• IPD Plan Description: There is no IPD sharing plan at this time.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No