Classification of Retinal Diseases by Artificial Intelligence
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Artificial Intelligence
- Sponsor
- Beijing Tongren Hospital
- Enrollment
- 1000000
- Locations
- 1
- Primary Endpoint
- F1 score
- Status
- Completed
- Last Updated
- 5 years ago
Overview
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.
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.
Investigators
Eligibility Criteria
Inclusion Criteria
- •fundus photography around 45° field which covers optic disc and macula
- •complete identification information
Exclusion Criteria
- •insufficient information for diagnosis.
Outcomes
Primary Outcomes
F1 score
Time Frame: 1 week
We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Area under curve
Time Frame: 1 week
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
Time Frame: 1 week
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
Time Frame: 1 week
We used positive predictive value and negative predictive value to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Secondary Outcomes
- Systemic biomarkers and diseases(1 week)