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NCT Number: NCT05981950

Real-world of AI 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.

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Key information

Conditions

Artificial Intelligence Eye Diseases Retinal Diseases

Age range

1 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Wen-Bin Wei

Beijing, Beijing Municipality, 100730, China

Location status: Recruiting

Location contact

Wen-Bin Wei, MD

CONTACT

[email protected]

58269516

Wen-Bin Wei, MD

PRINCIPAL_INVESTIGATOR

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

  • insufficient information for diagnosis

Treatment and study plan

artificial intelligence algorithm

Diagnostic Test

Retinal diseases diagnosed by artificial intelligence algorithm

Primary outcomes

  1. 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.

  2. 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.

  3. 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.

  4. 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Ruiheng Zhang, MD

CONTACT

[email protected]

18801121782

Wenbin Wei, MD

CONTACT

[email protected]

58269516

Sponsors and collaborators

Lead sponsor

Beijing Tongren Hospital

Other

Registry information

Official study title

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

Important dates

Study start
2023
Primary completion
2028
Study completion
2029
First posted
Aug 8, 2023
Registry last updated
Aug 8, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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