Purpose: Different methods have been reported for visual field (VF) estimation from optical
coherence tomography (OCT) or forecasting future VF using prior VFs. These methods are …

Purpose: Accurate assessment of disease progression is essential for glaucoma
mangement. The purpose of this study was to investigate how far in the future a deep …

Purpose: Lengthy and unreliable visual field (VF) testing presents a burden to both
glaucoma patient and clinician. We retrospectively evaluated the ability to predict VF loss …

Visual field tests (VFT) are pivotal for glaucoma diagnosis and conducted regularly to
monitor disease progression. Here we address the question to what degree aggregate VFT …

Purpose: We assess whether we can forecast future rapid visual field (VF) worsening using
deep learning models (DLM) trained on baseline VF, OCT, and clinical data. Additionally, we …

Purpose: To develop a deep learning model to estimate the visual field (VF) from spectral-
domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) and to …

Purpose: Standard automated perimetry is the gold standard to monitor visual field (VF) loss
in glaucoma management, but it is prone to intrasubject variability. We trained and validated …

Purpose To develop a deep learning method to predict visual field (VF) from wide-angle
swept-source optical coherence tomography (SS-OCT) and compare the performance of …

Background/Aim To train and validate the prediction performance of the deep learning (DL)
model to predict visual field (VF) in central 10° from spectral domain optical coherence …

Purpose Two novel deep learning methods using a convolutional neural network (CNN) and
a recurrent neural network (RNN) have recently been developed to forecast future visual …