Artificial Intelligence for Detecting Retinal Diseases
Classification of Retinal Diseases by Artificial Intelligence
1 other identifier
observational
1,000,000
1 country
1
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jun 2018
Typical duration for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
June 1, 2018
CompletedPrimary Completion
Last participant's last visit for primary outcome
June 30, 2020
CompletedStudy Completion
Last participant's last visit for all outcomes
October 1, 2020
CompletedFirst Submitted
Initial submission to the registry
December 16, 2020
CompletedFirst Posted
Study publicly available on registry
December 21, 2020
CompletedApril 15, 2021
June 1, 2018
2.1 years
December 16, 2020
April 12, 2021
Conditions
Outcome Measures
Primary Outcomes (4)
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 Outcomes (1)
Systemic biomarkers and diseases
1 week
Study Arms (1)
Retinal diseases diagnosed by artificial intelligence algorithm
Retinal diseases diagnosed by artificial intelligence algorithm
Interventions
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.
Eligibility Criteria
The study population is derived from an anonymous database that contains health examination results of the general population.
You may qualify if:
- fundus photography around 45° field which covers optic disc and macula
- complete identification information
You may not qualify if:
- insufficient information for diagnosis.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Wen-Bin Wei
Beijing, Beijing Municipality, 100730, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- STUDY CHAIR
Wenbin Wei
Beijing Tongren Hospital
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
December 16, 2020
First Posted
December 21, 2020
Study Start
June 1, 2018
Primary Completion
June 30, 2020
Study Completion
October 1, 2020
Last Updated
April 15, 2021
Record last verified: 2018-06
Data Sharing
- IPD Sharing
- Will not share