Diagnostic Efficacy of CNN in Differentiation of Visual Field

Study Overview
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Brief Summary
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Glaucoma is currently the leading cause of irreversible blindness in the world. The multi-center study is designed to evaluate the efficacy of the convolutional neural network based algorithm in differentiation of glaucomatous from non-glaucomatous visual field, and to assess its utility in the real world.

Detailed Description
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Glaucoma is the world's leading cause of irreversible blind, characterized by progressive retinal nerve fiber layer thinning and visual field defects. Visual field test is one of the gold standards for diagnosis and evaluation of progression of glaucoma. However, there is no universally accepted standard for the interpretation of visual field results, which is subjective and requires a large amount of experience. At present, artificial intelligence has achieved the accuracy comparable to human physicians in the interpretation of medical imaging of many different diseases. Previously, we have trained a deep convolutional neural network to read the visual field reports, which has even higher diagnostic efficacy than ophthalmologists. The current multi-center study is designed to evaluate the efficacy of the convolutional neural network based algorithm in differentiation of glaucomatous from non-glaucomatous visual field, compare its performance with ophthalmologists and to assess its utility in the real world.

Official Title
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Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Differentiation of Glaucomatous Visual Field From Non-glaucomatous Visual Field

Conditions
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Diagnositic Efficacy of Deep Convolutional Neural Network in Differentiation of Glaucoma Visual Field From Non-glaucoma Visual Field

Intervention / Treatment
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* Diagnostic Test: AI diagnostic algorithm


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age≥18; Informed consent obtained; Diagnosed with specific ocular diseases; Able to perform visual field test Exclusion Criteria: Incomplete clinical data to support diagnosis

Ages Eligible for Study
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Minimum Age: 18 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 |
| --- | --- |
| AI group<br>The visual field reports in this group will be evaluated by the convolutional neural network. | Diagnostic Test: AI diagnostic algorithm<br>* The visual fields collected would be assessed by the algorithm and ophthalmologists independently. The performance of the algorithm and the ophthalmologists would be compared, including accuracy, AUC, sensitivity and specificity.<br>* Other names: Standard diagnostic procedure;|
| Human group<br>The visual field reports in this group will be evaluated by 3 ophthalmologists independently. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| AUC value of convolutional neural network in differentiation of Glaucoma visual field from non-glaucoma visual field |  | from Jan 2019 to Jan 2020 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Sensitivity and specificity of convolutional neural network in detection of glaucoma visual field |  | from Jan 2019 to Jan 2020 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Deep convolutional neural network, Visual field, Glaucoma