Artificial Intelligence for Detecting Retinal Diseases

Overview

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.

Full Title of Study: “Classification of Retinal Diseases by Artificial Intelligence”

Study Type

  • Study Type: Observational
  • Study Design
    • Time Perspective: Prospective
  • Study Primary Completion Date: June 30, 2020

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

Interventions

  • Diagnostic Test: 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.

Arms, Groups and Cohorts

  • Retinal diseases diagnosed by artificial intelligence algorithm
    • Retinal diseases diagnosed by artificial intelligence algorithm

Clinical Trial Outcome Measures

Primary Measures

  • 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.
  • 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.
  • 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.
  • 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 Measures

  • 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.

Participating in This Clinical Trial

Inclusion Criteria

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

  • insufficient information for diagnosis.

Gender Eligibility: All

Minimum Age: 18 Years

Maximum Age: 80 Years

Are Healthy Volunteers Accepted: Accepts Healthy Volunteers

Investigator Details

  • Lead Sponsor
    • Beijing Tongren Hospital
  • Collaborator
    • Beijing Tulip Partner Technology Co., Ltd, China
  • Provider of Information About this Clinical Study
    • Sponsor
  • Overall Official(s)
    • Wenbin Wei, Study Chair, Beijing Tongren Hospital

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