Real-world of AI in Diagnosing Retinal Diseases
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| 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. |
Diagnostic Test: artificial intelligence algorithm
Retinal diseases diagnosed by artificial intelligence algorithm
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Outcome Measures
Primary Outcome Measures
- 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.
Eligibility Criteria
Criteria
Inclusion Criteria:
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fundus photography around 45° field which covers optic disc and macula
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complete identification information
Exclusion Criteria:
- insufficient information for diagnosis
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Wen-Bin Wei | Beijing | Beijing | China | 100730 |
Sponsors and Collaborators
- Beijing Tongren Hospital
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- Real-world RAIDS