Deep Learning Estimation of 24-2 Visual Field Map From Optic Nerve Head Optical Coherence Tomography Angiography
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence accurately estimates visual field maps using OCTA images. This deep learning approach may reduce the need for frequent visual field testing.
Area Of Science
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background
- Visual field (VF) testing is crucial for diagnosing and monitoring glaucoma and other optic nerve head (ONH) conditions.
- Optical coherence tomography angiography (OCTA) provides detailed microvascular information of the ONH.
- Current VF testing can be time-consuming and may not capture subtle changes effectively.
Purpose Of The Study
- To develop and evaluate deep learning (DL) models for estimating 24-2 visual field (VF) maps from OCTA optic nerve head (ONH) en face images.
- To compare the performance of DL models against traditional linear regression (LR) methods.
Main Methods
- Trained DL models on 3148 VF OCTA image pairs from 994 participants, utilizing radial peripapillary capillary (RPC), superficial, and choroidal ONH vascular density (VD) layers.
- Estimated 24-2 mean deviation (MD), pattern standard deviation (PSD), total deviation (TD), and pattern deviation (PD) values.
- Assessed model accuracy using mean absolute error (MAE) and Pearson correlation coefficient (R).
Main Results
- DL models significantly outperformed LR models in estimating VF values across all tested ONH layers (P <0.001).
- Using RPC, DL achieved an R of 0.79 and MAE of 1.77 dB for MD estimation.
- DL models using combined ONH layers showed slight improvements for MD and TD estimations compared to individual layers.
Conclusions
- Deep learning models applied to OCTA images demonstrate high accuracy in estimating 24-2 visual field maps.
- Leveraging ONH microvascular information from OCTA via DL holds potential for reducing the frequency of VF testing.
- This AI-driven approach may offer a more efficient method for monitoring optic nerve health.
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