Artificial Intelligence, Retinal Diseases
Conditions
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.
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_TESTartificial intelligence algorithm
Retinal diseases diagnosed by artificial intelligence algorithm
Sponsors
Beijing Tongren Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
1 Years to 100 Years
Inclusion criteria
* fundus photography around 45° field which covers optic disc and macula * complete identification information
Exclusion criteria
* insufficient information for diagnosis
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area under curve | 1 month | 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 | 1 month | 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 | 1 month | 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 | 1 month | We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases. |
Countries
China
Contacts
Primary ContactWenbin Wei, MD
Backup ContactRuiheng Zhang, MD
Outcome results
None listed