Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices.

Journal: Translational vision science & technology

Published Date: Jun 1, 2021

Abstract

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 compare the performance between them.

Authors

Jonghoon Shin

SK bioscience, Seonam-si, Republic of Korea.
Sungjoon Kim

Department of Ophthalmology, Pusan National University College of Medicine, Busan, Korea.
Jinmi Kim

Department of Biostatistics, Clinical Trial Center.
Keunheung Park

Department of Ophthalmology, Pusan National University College of Medicine.

Keywords

Algorithms Cross-Sectional Studies Deep Learning Intraocular Pressure Nerve Fibers Retinal Ganglion Cells Tomography, Optical Coherence Visual Fields

External Resources

View on PubMed Access via DOI PubMed (34086043)

Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices.

Abstract

Authors

Keywords

External Resources

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