Literature DB >> 30119782

Deep learning in ophthalmology: a review.

Parampal S Grewal1, Faraz Oloumi2, Uriel Rubin1, Matthew T S Tennant3.   

Abstract

Deep learning is an emerging technology with numerous potential applications in Ophthalmology. Deep learning tools have been applied to different diagnostic modalities including digital photographs, optical coherence tomography, and visual fields. These tools have demonstrated utility in assessment of various disease processes including cataracts, glaucoma, age-related macular degeneration, and diabetic retinopathy. Deep learning techniques are evolving rapidly, and will become more integrated into ophthalmic care. This article reviews the current evidence for deep learning in ophthalmology, and discusses future applications, as well as potential drawbacks.
Copyright © 2018 Canadian Ophthalmological Society. Published by Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2018        PMID: 30119782     DOI: 10.1016/j.jcjo.2018.04.019

Source DB:  PubMed          Journal:  Can J Ophthalmol        ISSN: 0008-4182            Impact factor:   1.882


  18 in total

1.  Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke.

Authors:  Li Xie; Song Yang; David Squirrell; Ehsan Vaghefi
Journal:  PLoS One       Date:  2020-04-10       Impact factor: 3.240

2.  Automatic screening of tear meniscus from lacrimal duct obstructions using anterior segment optical coherence tomography images by deep learning.

Authors:  Hitoshi Imamura; Hitoshi Tabuchi; Daisuke Nagasato; Hiroki Masumoto; Hiroaki Baba; Hiroki Furukawa; Sachiko Maruoka
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  2021-02-12       Impact factor: 3.117

3.  Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning.

Authors:  Greg Zaharchuk
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-29       Impact factor: 9.236

4.  Application of Deep Learning for Automated Detection of Polypoidal Choroidal Vasculopathy in Spectral Domain Optical Coherence Tomography.

Authors:  Papis Wongchaisuwat; Ranida Thamphithak; Peerakarn Jitpukdee; Nida Wongchaisuwat
Journal:  Transl Vis Sci Technol       Date:  2022-10-03       Impact factor: 3.048

5.  Segmentation and Evaluation of Corneal Nerves and Dendritic Cells From In Vivo Confocal Microscopy Images Using Deep Learning.

Authors:  Md Asif Khan Setu; Stefan Schmidt; Gwen Musial; Michael E Stern; Philipp Steven
Journal:  Transl Vis Sci Technol       Date:  2022-06-01       Impact factor: 3.048

6.  Automatic Detection of Diabetic Retinopathy in Retinal Fundus Photographs Based on Deep Learning Algorithm.

Authors:  Feng Li; Zheng Liu; Hua Chen; Minshan Jiang; Xuedian Zhang; Zhizheng Wu
Journal:  Transl Vis Sci Technol       Date:  2019-11-12       Impact factor: 3.283

7.  Digital image processing software for diagnosing diabetic retinopathy from fundus photograph.

Authors:  Tanapat Ratanapakorn; Athiwath Daengphoonphol; Nawapak Eua-Anant; Yosanan Yospaiboon
Journal:  Clin Ophthalmol       Date:  2019-04-17

8.  An Evaluation System of Fundus Photograph-Based Intelligent Diagnostic Technology for Diabetic Retinopathy and Applicability for Research.

Authors:  Wei-Hua Yang; Bo Zheng; Mao-Nian Wu; Shao-Jun Zhu; Fang-Qin Fei; Ming Weng; Xian Zhang; Pei-Rong Lu
Journal:  Diabetes Ther       Date:  2019-07-09       Impact factor: 2.945

Review 9.  Discovery and clinical translation of novel glaucoma biomarkers.

Authors:  Gala Beykin; Anthony M Norcia; Vivek J Srinivasan; Alfredo Dubra; Jeffrey L Goldberg
Journal:  Prog Retin Eye Res       Date:  2020-07-10       Impact factor: 21.198

10.  Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography.

Authors:  Takahiro Sogawa; Hitoshi Tabuchi; Daisuke Nagasato; Hiroki Masumoto; Yasushi Ikuno; Hideharu Ohsugi; Naofumi Ishitobi; Yoshinori Mitamura
Journal:  PLoS One       Date:  2020-04-16       Impact factor: 3.240

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