Literature DB >> 15370551

A comparison of digital retinal image quality among photographers with different levels of training using a non-mydriatic fundus camera.

David Maberley1, Andrew Morris, Dawn Hay, Angela Chang, Laura Hall, Naresh Mandava.   

Abstract

PURPOSE: To evaluate the quality of digital retinal images taken by three photographers with different levels of photographic training, using a non-mydriatic fundus camera.
METHODS: This study compares 45-degree digital retinal images taken with a non-mydriatic fundus camera by three different photographers with different levels of photographic training: (I) A professional ophthalmic photographer with 20 years of experience; (2) a non-professional photographer with 2 days of photographic training and experience with 50 patients; (3) a non-professional photographer with 1 hour of photographic training and experience with 10 patients. The quality of the photographs was evaluated by the consensus of two retina specialists.
RESULTS: Sixty-four (64) eyes of 33 subjects were imaged by the three photographers for a total of 192 images. Thirty-four eyes were photographed in the non-dilated state. The trained ophthalmic photographer and the two non-professional photographers did not have statistically significant differences in image quality based on the image evaluations. (Chi-square P-value: 0.57). This finding was consistent for eyes in both the non-dilated and dilated state.
CONCLUSIONS: Fundus image quality for images taken with a non-mydriatic camera were not significantly different among three photographers with different levels of training.

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Year:  2004        PMID: 15370551     DOI: 10.1080/09286580490514496

Source DB:  PubMed          Journal:  Ophthalmic Epidemiol        ISSN: 0928-6586            Impact factor:   1.648


  8 in total

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Authors:  Cynthia Owsley; Gerald McGwin; David J Lee; Byron L Lam; David S Friedman; Emily W Gower; Julia A Haller; Lisa A Hark; Jinan Saaddine
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2.  Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity.

Authors:  Aaron S Coyner; Ryan Swan; James M Brown; Jayashree Kalpathy-Cramer; Sang Jin Kim; J Peter Campbell; Karyn E Jonas; Susan Ostmo; R V Paul Chan; Michael F Chiang
Journal:  AMIA Annu Symp Proc       Date:  2018-12-05

3.  Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.

Authors:  Aaron S Coyner; Ryan Swan; J Peter Campbell; Susan Ostmo; James M Brown; Jayashree Kalpathy-Cramer; Sang Jin Kim; Karyn E Jonas; R V Paul Chan; Michael F Chiang
Journal:  Ophthalmol Retina       Date:  2019-01-31

4.  Using Retinal Imaging to Study Dementia.

Authors:  Victor T T Chan; Tiffany H K Tso; Fangyao Tang; Clement Tham; Vincent Mok; Christopher Chen; Tien Y Wong; Carol Y Cheung
Journal:  J Vis Exp       Date:  2017-11-06       Impact factor: 1.355

Review 5.  The use of retinal photography in nonophthalmic settings and its potential for neurology.

Authors:  Mario A Pérez; Beau B Bruce; Nancy J Newman; Valérie Biousse
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6.  Non-mydriatic ocular fundus photography and telemedicine: past, present, and future.

Authors:  Beau B Bruce; Nancy J Newman; Mario A Pérez; Valérie Biousse
Journal:  Neuroophthalmology       Date:  2013-04-01

7.  A Teleophthalmology Support System Based on the Visibility of Retinal Elements Using the CNNs.

Authors:  Gustavo Calderon-Auza; Cesar Carrillo-Gomez; Mariko Nakano; Karina Toscano-Medina; Hector Perez-Meana; Ana Gonzalez-H Leon; Hugo Quiroz-Mercado
Journal:  Sensors (Basel)       Date:  2020-05-16       Impact factor: 3.576

8.  Fundoscopy use in neurology departments and the utility of smartphone photography: a prospective prevalence and crossover diagnostic accuracy study amongst neurology inpatients.

Authors:  George He; Hamish P Dunn; Kate E Ahmad; Eloise Watson; Andrew Henderson; Dominique Tynan; John Leaney; Andrew J White; Alex W Hewitt; Clare L Fraser
Journal:  Eur J Neurol       Date:  2022-05-31       Impact factor: 6.288

  8 in total

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