Literature DB >> 29675699

[Deep learning and neuronal networks in ophthalmology : Applications in the field of optical coherence tomography].

M Treder1, N Eter2.   

Abstract

Deep learning is increasingly becoming the focus of various imaging methods in medicine. Due to the large number of different imaging modalities, ophthalmology is particularly suitable for this field of application. This article gives a general overview on the topic of deep learning and its current applications in the field of optical coherence tomography. For the benefit of the reader it focuses on the clinical rather than the technical aspects.

Keywords:  Future potential; Glaucoma; Imaging; Machine learning; Macular edema

Mesh:

Year:  2018        PMID: 29675699     DOI: 10.1007/s00347-018-0706-0

Source DB:  PubMed          Journal:  Ophthalmologe        ISSN: 0941-293X            Impact factor:   1.059


  33 in total

1.  Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis.

Authors:  D S Grewal; R Jain; S P S Grewal; V Rihani
Journal:  Eur J Ophthalmol       Date:  2008 Nov-Dec       Impact factor: 2.597

2.  Automated anterior chamber angle localization and glaucoma type classification in OCT images.

Authors:  Yanwu Xu; Jiang Liu; Jun Cheng; Beng Hai Lee; Damon Wing Kee Wong; Mani Baskaran; Shamira Perera; Tin Aung
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2013

3.  Optical coherence tomography.

Authors:  D Huang; E A Swanson; C P Lin; J S Schuman; W G Stinson; W Chang; M R Hee; T Flotte; K Gregory; C A Puliafito
Journal:  Science       Date:  1991-11-22       Impact factor: 47.728

4.  Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning.

Authors:  Maximilian Treder; Jost Lennart Lauermann; Nicole Eter
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  2017-11-20       Impact factor: 3.117

5.  A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head.

Authors:  Sripad Krishna Devalla; Khai Sing Chin; Jean-Martial Mari; Tin A Tun; Nicholas G Strouthidis; Tin Aung; Alexandre H Thiéry; Michaël J A Girard
Journal:  Invest Ophthalmol Vis Sci       Date:  2018-01-01       Impact factor: 4.799

6.  Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning.

Authors:  Thomas Schlegl; Sebastian M Waldstein; Hrvoje Bogunovic; Franz Endstraßer; Amir Sadeghipour; Ana-Maria Philip; Dominika Podkowinski; Bianca S Gerendas; Georg Langs; Ursula Schmidt-Erfurth
Journal:  Ophthalmology       Date:  2017-12-08       Impact factor: 12.079

7.  Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects.

Authors:  Hassan Muhammad; Thomas J Fuchs; Nicole De Cuir; Carlos G De Moraes; Dana M Blumberg; Jeffrey M Liebmann; Robert Ritch; Donald C Hood
Journal:  J Glaucoma       Date:  2017-12       Impact factor: 2.503

8.  A hierarchical framework for estimating neuroretinal rim area using 3D spectral domain optical coherence tomography (SD-OCT) optic nerve head (ONH) images of healthy and glaucoma eyes.

Authors:  Akram Belghith; Christopher Bowd; Robert N Weinreb; Linda M Zangwill
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2014

9.  Use of a Neural Net to Model the Impact of Optical Coherence Tomography Abnormalities on Vision in Age-related Macular Degeneration.

Authors:  Tariq M Aslam; Haider R Zaki; Sajjad Mahmood; Zaria C Ali; Nur A Ahmad; Mariana R Thorell; Konstantinos Balaskas
Journal:  Am J Ophthalmol       Date:  2017-10-31       Impact factor: 5.258

10.  Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes.

Authors:  Gábor Márk Somfai; Erika Tátrai; Lenke Laurik; Boglárka Varga; Veronika Ölvedy; Hong Jiang; Jianhua Wang; William E Smiddy; Anikó Somogyi; Delia Cabrera DeBuc
Journal:  BMC Bioinformatics       Date:  2014-04-12       Impact factor: 3.169

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  4 in total

1.  Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis.

Authors:  Hina Raja; M Usman Akram; Arslan Shaukat; Shoab Ahmed Khan; Norah Alghamdi; Sajid Gul Khawaja; Noman Nazir
Journal:  J Digit Imaging       Date:  2020-09-23       Impact factor: 4.056

Review 2.  [Diagnostics of diseases of the optic nerve head in times of artificial intelligence and big data].

Authors:  R Diener; M Treder; N Eter
Journal:  Ophthalmologe       Date:  2021-04-22       Impact factor: 1.059

3.  [Employee survey after introduction of the FIDUS electronic patient file at the Saarland University Eye Hospital].

Authors:  Amine Maamri; Fabian N Fries; Corinna Spira-Eppig; Timo Eppig; Berthold Seitz
Journal:  Ophthalmologe       Date:  2021-10-27       Impact factor: 1.174

4.  Magnetic Resonance Imaging Images under Deep Learning in the Identification of Tuberculosis and Pneumonia.

Authors:  Yabin Liu; Yimin Wang; Ya Shu; Jing Zhu
Journal:  J Healthc Eng       Date:  2021-12-15       Impact factor: 2.682

  4 in total

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