Diagnositic Efficacy of Deep Convolutional Neural Network in Differentiation of Glaucoma Visual Field From Non-glaucoma Visual Field
Conditions
Keywords
Deep convolutional neural network, Visual field, Glaucoma
Brief summary
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
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.
Interventions
DIAGNOSTIC_TESTAI diagnostic algorithm
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.
Sponsors
Sun Yat-sen University
Study design
Observational model
OTHER
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Age≥18; 2. Informed consent obtained; 3. Diagnosed with specific ocular diseases; 4. Able to perform visual field test
Exclusion criteria
Incomplete clinical data to support diagnosis
Design outcomes
Primary
| Measure | 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
| Measure | Time frame |
|---|---|
| Sensitivity and specificity of convolutional neural network in detection of glaucoma visual field | from Jan 2019 to Jan 2020 |
Countries
China
Outcome results
None listed