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OpenTrials
Completed

NCT Number: NCT04678375

Artificial Intelligence for Detecting Retinal Diseases

The objective of this study is to apply an artificial intelligence algorithm to diagnose multi retinal diseases from fundus photography. The effectiveness and accuracy of this algorithm was 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

18 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Wen-Bin Wei

Beijing, Beijing Municipality, 100730, China

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. The effectiveness and accuracy of this algorithm was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, area under curve, and F1 score.

Who can participate

Healthy volunteers accepted: Yes

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

Retinal diseases diagnosed by artificial intelligence algorithm

Diagnostic Test

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.

Primary outcomes

  1. Area under curve

    Time frame: 1 week

    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 week

    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 week

    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 week

    We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.

Secondary outcomes

  1. Systemic biomarkers and diseases

    Time frame: 1 week

    Using medical records as the gold standard, we test the accuracy of this artificial intelligence algorism recognition and classification of systemic biomarkers and diseases: age, sex, blood pressure, blood hemoglobin, cardiovascular diseases, thyroid function and kidney function.

Sponsors and collaborators

Lead sponsor

Beijing Tongren Hospital

Other

Collaborators

  • Beijing Tulip Partner Technology Co., Ltd, China

Registry information

Official study title

Classification of Retinal Diseases by Artificial Intelligence

Important dates

Study start
2018
Primary completion
2020
Study completion
2020
First posted
Dec 21, 2020
Registry last updated
Apr 15, 2021

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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