Artificial Intelligence for Detecting Retinal Diseases

Classification of Retinal Diseases by Artificial Intelligence

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.

Study Overview

• Status: Completed
• Conditions: Retinal Diseases; Artificial Intelligence
• Intervention / Treatment: Diagnostic test: Retinal diseases diagnosed by artificial intelligence algorithm

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.

• Study Type: Observational
• Enrollment (Actual): 1000000

Contacts and Locations

Study Locations

• China
  • Beijing
    • Beijing, Beijing, China, 100730
      • Wen-Bin Wei

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 80 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• 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

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Retinal diseases diagnosed by artificial intelligence algorithm

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Area under curve	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 week
Sensitivity and specificity	We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.	1 week
Positive predictive value, negative predictive value	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 week
F1 score	We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.	1 week

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Systemic biomarkers and diseases	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.	1 week

Collaborators and Investigators

• Sponsor: Beijing Tongren Hospital
• Collaborators: Beijing Tulip Partner Technology Co., Ltd, China
• Investigators: Study Chair: Wenbin Wei, Beijing Tongren Hospital

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2018
• Primary Completion (Actual): June 30, 2020
• Study Completion (Actual): October 1, 2020

Study Registration Dates

• First Submitted: December 16, 2020
• First Submitted That Met QC Criteria: December 16, 2020
• First Posted (Actual): December 21, 2020

Study Record Updates

• Last Update Posted (Actual): April 15, 2021
• Last Update Submitted That Met QC Criteria: April 12, 2021
• Last Verified: June 1, 2018

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Eye Diseases; Retinal Diseases
• Other Study ID Numbers: AI in retinal diseases

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