  Background/Aims: Standard Automated Perimetry (SAP) is the gold standard to
monitor visual field (VF) loss in glaucoma management, but is prone to
intra-subject variability. We developed and validated a deep learning (DL)
regression model that estimates pointwise and overall VF loss from unsegmented
optical coherence tomography (OCT) scans. Methods: Eight DL regression models
were trained with various retinal imaging modalities: circumpapillary OCT at
3.5mm, 4.1mm, 4.7mm diameter, and scanning laser ophthalmoscopy (SLO) en face
images to estimate mean deviation (MD) and 52 threshold values. This
retrospective study used data from patients who underwent a complete glaucoma
examination, including a reliable Humphrey Field Analyzer (HFA) 24-2 SITA
Standard VF exam and a SPECTRALIS OCT scan using the Glaucoma Module Premium
Edition. Results: A total of 1378 matched OCT-VF pairs of 496 patients (863
eyes) were included for training and evaluation of the DL models. Average
sample MD was -7.53dB (from -33.8dB to +2.0dB). For 52 VF threshold values
estimation, the circumpapillary OCT scan with the largest radius (4.7mm)
achieved the best performance among all individual models (Pearson r=0.77, 95%
CI=[0.72-0.82]). For MD, prediction averaging of OCT-trained models (3.5mm,
4.1mm, 4.7mm) resulted in a Pearson r of 0.78 [0.73-0.83] on the validation set
and comparable performance on the test set (Pearson r=0.79 [0.75-0.82]).
Conclusion: DL on unsegmented OCT scans accurately predicts pointwise and mean
deviation of 24-2 VF in glaucoma patients. Automated VF from unsegmented OCT
could be a solution for patients unable to produce reliable perimetry results.
