Deep Learning Estimation of 24-2 Visual Field Map from Optic Nerve Head Optical Coherence Tomography Angiography.

Precis: Artificial intelligence applied to OCTA images demonstrated high accuracy in estimating 24-2 visual field maps by leveraging information from pararpapillary area.

Purpose: To develop deep learning (DL) models estimating 24-2 visual field (VF) maps from optical coherence tomography angiography (OCTA) optic nerve head (ONH) en face images.

Methods: A total of 3148 VF OCTA pairs were collected from 994 participants (1684 eyes). DL models were trained using radial peripapillary capillary (RPC), superficial, and choroidal, as well as combined ONH VD layers, to estimate 24-2 mean deviation (MD), pattern standard deviation (PSD), 52 total deviation (TD), and pattern deviation (PD) values and compared with a linear regression (LR) model. Model accuracy was assessed by calculating mean absolute error (MAE) and R (Pearson correlation coefficients) between estimated and actual VF values.

Results: DL models outperformed LR estimates for the estimation of VF values using individual and combined layers (P<0.001). For example, in the estimation of MD using RPC, DL achieved an R of 0.79 and MAEs of 1.77 dB. Average estimated TDs using RPC had R of 0.63 and MAEs of 3.08 dB. DL estimation using combined layers slightly improved choroid in the estimation of MD (P<0.01) and had comparable performance with RPC and superficial layers. It also slightly improved RPC, superficial and choroidal layer in the estimation of TDs (P<0.01).

Conclusions: DL models from OCTA images demonstrated high accuracy in estimating 24-2 VF maps by leveraging information from ONH layers. By extending the application of DL to OCTA images using RPC or superficial layers, it may be possible to reduce the frequency of VF testing to individual patients.