Real-world of AI in Diagnosing Retinal Diseases

NCT ID: NCT05981950

Last Updated: 2023-08-08

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

100000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-08-01

Study Completion Date

2029-08-01

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Artificial Intelligence Retinal Diseases

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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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.

artificial intelligence algorithm

Intervention Type DIAGNOSTIC_TEST

Retinal diseases diagnosed by artificial intelligence algorithm

Interventions

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artificial intelligence algorithm

Retinal diseases diagnosed by artificial intelligence algorithm

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

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

Exclusion Criteria

* insufficient information for diagnosis
Minimum Eligible Age

1 Year

Maximum Eligible Age

100 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Beijing Tongren Hospital

OTHER

Sponsor Role lead

Responsible Party

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Wenbin Wei

Prof

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Wen-Bin Wei

Beijing, Beijing Municipality, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Wenbin Wei, MD

Role: CONTACT

Ruiheng Zhang, MD

Role: CONTACT

18801121782

Facility Contacts

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Wen-Bin Wei, MD

Role: primary

Other Identifiers

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Real-world RAIDS

Identifier Type: -

Identifier Source: org_study_id

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