Real-world of AI in Diagnosing Retinal Diseases
Real-world Application of Using Artificial Intelligence in Diagnosing Retinal Diseases
1 other identifier
observational
100,000
1 country
1
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Aug 2023
Longer than P75 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
First Submitted
Initial submission to the registry
August 1, 2023
CompletedStudy Start
First participant enrolled
August 1, 2023
CompletedFirst Posted
Study publicly available on registry
August 8, 2023
CompletedPrimary Completion
Last participant's last visit for primary outcome
August 1, 2028
ExpectedStudy Completion
Last participant's last visit for all outcomes
August 1, 2029
August 8, 2023
August 1, 2023
5 years
August 1, 2023
August 1, 2023
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 month
Sensitivity and specificity
We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
1 month
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 month
F1 score
We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
1 month
Study Arms (1)
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.
Interventions
Retinal diseases diagnosed by artificial intelligence algorithm
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)
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Prof
Study Record Dates
First Submitted
August 1, 2023
First Posted
August 8, 2023
Study Start
August 1, 2023
Primary Completion (Estimated)
August 1, 2028
Study Completion (Estimated)
August 1, 2029
Last Updated
August 8, 2023
Record last verified: 2023-08
Data Sharing
- IPD Sharing
- Will not share