Artificial Intelligence for Detecting Retinal Diseases

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Classification of Retinal Diseases by Artificial Intelligence

Conditions
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Retinal Diseases, Artificial Intelligence

Intervention / Treatment
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* Diagnostic Test: Retinal diseases diagnosed by artificial intelligence algorithm


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: fundus photography around 45° field which covers optic disc and macula complete identification information Exclusion Criteria: insufficient information for diagnosis.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Retinal diseases diagnosed by artificial intelligence algorithm<br>Retinal diseases diagnosed by artificial intelligence algorithm | Diagnostic Test: Retinal diseases diagnosed by artificial intelligence algorithm<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | 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 | 

Secondary Outcome Measures

| Outcome Measure | 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 |