Artificial Intelligence for Detecting 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 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.
| 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. The effectiveness and accuracy of this algorithm was 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 Retinal diseases diagnosed by artificial intelligence algorithm |
Diagnostic Test: 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.
|
Outcome Measures
Primary Outcome Measures
- Area under curve [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 [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 [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.
- F1 score [1 week]
We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Secondary Outcome Measures
- Systemic biomarkers and diseases [1 week]
Using medical records as the gold standard, we test the accuracy of this artificial intelligence algorism recognition and classification of systemic biomarkers and diseases: age, sex, blood pressure, blood hemoglobin, cardiovascular diseases, thyroid function and kidney function.
Eligibility Criteria
Criteria
Inclusion Criteria:
-
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
- Beijing Tulip Partner Technology Co., Ltd, China
Investigators
- Study Chair: Wenbin Wei, Beijing Tongren Hospital
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- AI in retinal diseases