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| Name | Class |
|---|---|
| Beijing Tulip Partner Technology Co., Ltd, China | UNKNOWN |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Retinal diseases diagnosed by artificial intelligence algorithm | Retinal diseases diagnosed by artificial intelligence algorithm |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Retinal diseases diagnosed by artificial intelligence algorithm | Diagnostic Test | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Area under curve | 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. | 1 week |
| Sensitivity and specificity | We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases. | 1 week |
| Positive predictive value, negative predictive value | We used positive predictive value and negative predictive value to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases. | 1 week |
| F1 score | We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases. | 1 week |
| Measure | Description | Time Frame |
|---|---|---|
| Systemic biomarkers and diseases | 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. | 1 week |
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Inclusion Criteria:
Exclusion Criteria:
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The study population is derived from an anonymous database that contains health examination results of the general population.
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| Name | Affiliation | Role |
|---|---|---|
| Wenbin Wei | Beijing Tongren Hospital | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Wen-Bin Wei | Beijing | Beijing Municipality | 100730 | China |
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| ID | Term |
|---|---|
| D012164 | Retinal Diseases |
| ID | Term |
|---|---|
| D005128 | Eye Diseases |
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