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Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
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Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning
Giovanni Montesano1,2, Georgios Lazaridis1,2,3, Giovanni Ometto1,2
1City, University of London, Optometry and Visual Sciences, London, UK.
Translational Vision Science & Technology
|October 13, 2023
Summary
Deep learning (DL) models improve glaucoma perimetry by using structure-function predictions from OCT scans. This enhanced approach significantly speeds up visual field testing and increases accuracy.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Glaucoma diagnosis and monitoring rely on visual field testing.
- Perimetric strategies aim to efficiently and accurately assess visual field sensitivity.
- Deep learning (DL) models offer potential for improving diagnostic tools.
Purpose of the Study:
- To evaluate a perimetric strategy enhanced by structure-function predictions from a DL model.
- To compare the speed and accuracy of DL-informed perimetry against traditional methods.
Main Methods:
- Utilized visual field test-retest data from 146 glaucoma patient eyes.
- Generated structure-function predictions using a DL model with OCT scans.
- Compared a standard Zippy Estimation by Sequential Testing (ZEST) strategy with a structure-informed ZEST (S-ZEST) strategy.
Main Results:
- S-ZEST demonstrated significantly higher speed (fewer presentations) and accuracy (lower mean absolute error) compared to ZEST.
- The inclusion of spatial correlations further enhanced both strategies.
- DL-based structure-function predictions notably improved perimetric test performance.
Conclusions:
- DL structure-function predictions derived from OCT scans significantly enhance perimetric tests in glaucoma patients.
- This advancement offers a more efficient and accurate method for visual field assessment.
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