Prediction of visual field from swept-source optical coherence tomography using deep learning algorithms.

Journal: Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie

Published Date: Nov 1, 2020

Abstract

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 three Google Inception architectures.

Authors

Keunheung Park

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

Department of Biostatistics, Clinical Trial Center.
Sangyoon Kim

Department of Ophthalmology, College of Medicine, Pusan National University Yangsan Hospital, Yangsan, South Korea.
Jonghoon Shin

SK bioscience, Seonam-si, Republic of Korea.

Keywords

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

External Resources

View on PubMed Access via DOI PubMed (32845372)

Prediction of visual field from swept-source optical coherence tomography using deep learning algorithms.

Abstract

Authors

Keywords

External Resources

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