Real-world of AI in Diagnosing Retinal Diseases

August 1, 2023 updated by: Wenbin Wei, Beijing Tongren Hospital

Real-world Application of Using Artificial Intelligence in Diagnosing Retinal Diseases

The objective of this study is to apply an artificial intelligence algorithm to diagnose multi-retinal diseases in real-world settings. The effectiveness and accuracy of this algorithm are evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

The objective of this study is to apply an artificial intelligence algorithm to diagnose referral diabetes retinopathy, referral age-related macular degeneration, referral possible glaucoma, pathological myopia, retinal vein occlusion, macular hole, macular epiretinal membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and other retinal lesions from fundus photography. tic 45-degree fundus cameras, trained operators took binocular fundus photography on participants. Operators were then asked to identify gradable images and unload for algorithm diagnosis. The effectiveness and accuracy of this algorithm are evaluated by sensitivity, specificity, positive predictive value, negative predictive value, area under curve, and F1 score.

Study Type

Observational

Enrollment (Estimated)

100000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • China
    • Beijing
      • Beijing, Beijing, China, 100730
        • Recruiting
        • Wen-Bin Wei
        • Contact:
        • Principal Investigator:
          • Wen-Bin Wei, MD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

The study population is derived from an anonymous database that contains health examination results of the general population.

Description

Inclusion Criteria:

  • fundus photography around 45° field which covers optic disc and macula
  • complete identification information

Exclusion Criteria:

  • insufficient information for diagnosis

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Retinal diseases diagnosed by artificial intelligence algorithm
An artificial intelligence algorithm was applied to diagnose referral diabetes retinopathy, referral age-related macular degeneration, referral possible glaucoma, pathological myopia, retinal vein occlusion, macular hole, macular epiretinal membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and other retinal lesions from fundus photography.
Diagnostic test: artificial intelligence algorithm
Retinal diseases diagnosed by artificial intelligence algorithm

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area under curve
Time Frame: 1 month
We used the receiver operating characteristic (ROC) curve and area under curve to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
1 month
Sensitivity and specificity
Time Frame: 1 month
We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
1 month
Positive predictive value, negative predictive value
Time Frame: 1 month
We used positive predictive value and negative predictive value to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
1 month
F1 score
Time Frame: 1 month
We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
1 month

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Beijing Tongren Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 1, 2023

Primary Completion (Estimated)

August 1, 2028

Study Completion (Estimated)

August 1, 2029

Study Registration Dates

First Submitted

August 1, 2023

First Submitted That Met QC Criteria

August 1, 2023

First Posted (Actual)

August 8, 2023

Study Record Updates

Last Update Posted (Actual)

August 8, 2023

Last Update Submitted That Met QC Criteria

August 1, 2023

Last Verified

August 1, 2023

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Real-world RAIDS

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Retinal Diseases

Clinical Trials on artificial intelligence algorithm

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in New Zealand

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe