Real-world of AI in Diagnosing Retinal Diseases
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: artificial intelligence algorithm.
- Who it may be relevant to
- Registry conditions: Artificial Intelligence, Retinal Diseases. Basic parameters: 1 year — 100 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Real-world Application of Using Artificial Intelligence in Diagnosing Retinal Diseases
Overview
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.
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. tic 45-degree fundus cameras, trained operators took binocular fundus photography on participants. Operators were then asked to identify gradable images and unload for algorithm diagnosis. The effectiveness and accuracy of this algorithm are evaluated by sensitivity, specificity, positive predictive value, negative predictive value, area under curve, and F1 score.
Interventions
- Diagnostic test artificial intelligence algorithm
Retinal diseases diagnosed by artificial intelligence algorithm
Primary outcome measures
- Area under curve [Time frame: 1 month]
- Sensitivity and specificity [Time frame: 1 month]
- Positive predictive value, negative predictive value [Time frame: 1 month]
- F1 score [Time frame: 1 month]
Eligibility criteria
Inclusion criteria
- fundus photography around 45° field which covers optic disc and macula
- complete identification information
Exclusion criteria
- insufficient information for diagnosis
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Wen-Bin Wei — Beijing
Identifiers
NCT: NCT05981950 · Real-world RAIDS