Literature DB >> 33224635

Diving Deep into Deep Learning: An Update on Artificial Intelligence in Retina.

Brian E Goldhagen1,2, Hasenin Al-Khersan1.   

Abstract

PURPOSE OF REVIEW: In the present article, we will provide an understanding and review of artificial intelligence in the subspecialty of retina and its potential applications within the specialty. RECENT
FINDINGS: Given the significant use of diagnostic imaging within retina, this subspecialty is a fitting area for the incorporation of artificial intelligence. Researchers have aimed at creating models to assist in the diagnosis and management of retinal disease as well as in the prediction of disease course and treatment response. Most of this work thus far has focused on diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity, although other retinal diseases have started to be explored as well.
SUMMARY: Artificial intelligence is well-suited to transform the practice of ophthalmology. A basic understanding of the technology is important for its effective implementation and growth.

Entities:  

Keywords:  Age-related macular degeneration, Retinopathy of prematurity; Artificial intelligence; Diabetic retinopathy; Machine learning; Neural networks

Year:  2020        PMID: 33224635      PMCID: PMC7679067          DOI: 10.1007/s40135-020-00240-2

Source DB:  PubMed          Journal:  Curr Ophthalmol Rep        ISSN: 2167-4868


  58 in total

1.  Diagnosis of plus disease in retinopathy of prematurity using Retinal Image multiScale Analysis.

Authors:  Rony Gelman; M Elena Martinez-Perez; Deborah K Vanderveen; Anne Moskowitz; Anne B Fulton
Journal:  Invest Ophthalmol Vis Sci       Date:  2005-12       Impact factor: 4.799

2.  Progression to severe retinopathy predicted by retinal vessel diameter between 31 and 34 weeks of postconception age.

Authors:  Michael P Rabinowitz; Juan E Grunwald; Karen A Karp; Graham E Quinn; Gui-Shuang Ying; Monte D Mills
Journal:  Arch Ophthalmol       Date:  2007-11

3.  Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application.

Authors:  Valentina Bellemo; Gilbert Lim; Tyler Hyungtaek Rim; Gavin S W Tan; Carol Y Cheung; SriniVas Sadda; Ming-Guang He; Adnan Tufail; Mong Li Lee; Wynne Hsu; Daniel Shu Wei Ting
Journal:  Curr Diab Rep       Date:  2019-07-31       Impact factor: 4.810

4.  Utility of Deep Learning Methods for Referability Classification of Age-Related Macular Degeneration.

Authors:  Phillippe Burlina; Neil Joshi; Katia D Pacheco; David E Freund; Jun Kong; Neil M Bressler
Journal:  JAMA Ophthalmol       Date:  2018-11-01       Impact factor: 7.389

5.  Telemedicine and Diabetic Retinopathy: Review of Published Screening Programs.

Authors:  Kevin Tozer; Maria A Woodward; Paula A Newman-Casey
Journal:  J Endocrinol Diabetes       Date:  2015-11-11

6.  Automated diabetic retinopathy detection using optical coherence tomography angiography: a pilot study.

Authors:  Harpal Singh Sandhu; Nabila Eladawi; Mohammed Elmogy; Robert Keynton; Omar Helmy; Shlomit Schaal; Ayman El-Baz
Journal:  Br J Ophthalmol       Date:  2018-01-23       Impact factor: 4.638

7.  Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography.

Authors:  Oscar Perdomo; Hernán Rios; Francisco J Rodríguez; Sebastián Otálora; Fabrice Meriaudeau; Henning Müller; Fabio A González
Journal:  Comput Methods Programs Biomed       Date:  2019-06-14       Impact factor: 5.428

Review 8.  The Potential Importance of Detection of Neovascular Age-Related Macular Degeneration When Visual Acuity Is Relatively Good.

Authors:  Allen C Ho; Thomas A Albini; David M Brown; David S Boyer; Carl D Regillo; Jeffrey S Heier
Journal:  JAMA Ophthalmol       Date:  2017-03-01       Impact factor: 7.389

9.  Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration.

Authors:  Philippe M Burlina; Neil Joshi; Katia D Pacheco; David E Freund; Jun Kong; Neil M Bressler
Journal:  JAMA Ophthalmol       Date:  2018-12-01       Impact factor: 7.389

10.  Automatic drusen quantification and risk assessment of age-related macular degeneration on color fundus images.

Authors:  Mark J J P van Grinsven; Yara T E Lechanteur; Johannes P H van de Ven; Bram van Ginneken; Carel B Hoyng; Thomas Theelen; Clara I Sánchez
Journal:  Invest Ophthalmol Vis Sci       Date:  2013-04-30       Impact factor: 4.799

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  2 in total

1.  [Use of artificial intelligence in screening for diabetic retinopathy at a tertiary diabetes center].

Authors:  Sebastian Paul; Allam Tayar; Ewa Morawiec-Kisiel; Beathe Bohl; Rico Großjohann; Elisabeth Hunfeld; Martin Busch; Johanna M Pfeil; Merlin Dähmcke; Tara Brauckmann; Sonja Eilts; Marie-Christine Bründer; Milena Grundel; Bastian Grundel; Frank Tost; Jana Kuhn; Jörg Reindel; Wolfgang Kerner; Andreas Stahl
Journal:  Ophthalmologie       Date:  2022-01-26

2.  Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning.

Authors:  Fukui Liang; Caiqin Li; Xiaoqin Fu
Journal:  J Healthc Eng       Date:  2021-08-17       Impact factor: 2.682

  2 in total

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