Literature DB >> 20484593

Digital versus film Fundus photography for research grading of diabetic retinopathy severity.

Helen K Li1, Larry D Hubbard, Ronald P Danis, Adol Esquivel, Jose F Florez-Arango, Nicola J Ferrier, Elizabeth A Krupinski.   

Abstract

PURPOSE: To assess agreement between digital and film photography for research classification of diabetic retinopathy severity.
METHODS: Digital and film photographs from a 152-eye cohort with a full spectrum of Early Treatment Diabetic Retinopathy Study (ETDRS) severity levels were assessed for repeatability of grading within each image medium and for agreement on ETDRS discrete severity levels, ascending severity thresholds, and presence or absence of diabetic retinopathy index lesions, between digital and 35-mm slides (film). Digital photographs were color balanced to match film.
RESULTS: There was substantial agreement (κ = 0.61, κ(w) [linear weighted] = 0.87) in classification of ETDRS diabetic retinopathy severity levels between digital images and film. Marginal homogeneity analyses found no significant difference in frequency distributions on the severity scale (P = 0.21, Bhapkar test). The κ results ranged from 0.72 to 0.95 for presence or absence of eight ascending diabetic retinopathy severity thresholds. Repeatability of grading between readers viewing digital images was equal to or better than that obtained with film (pair-wise interreader κ for digital images ranged from 0.47 to 0.57 and for film from 0.43 to 0.57. The κ results for identifying diabetic retinopathy lesions ranged from moderate to almost perfect. Moderate agreement of intraretinal microvascular abnormalities and venous beading between digital images and film accounted for slightly lower concordance for severity thresholds ≥47 and for slightly lower interreader agreement within digital and film images at severity thresholds ≥43 and ≥47.
CONCLUSIONS: Under controlled circumstances, digital photography can equal the reliability of 35-mm slides for research classification of ETDRS severity level.

Entities:  

Mesh:

Year:  2010        PMID: 20484593     DOI: 10.1167/iovs.09-4803

Source DB:  PubMed          Journal:  Invest Ophthalmol Vis Sci        ISSN: 0146-0404            Impact factor:   4.799


  10 in total

1.  Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images.

Authors:  Carla Agurto; E Simon Barriga; Victor Murray; Sheila Nemeth; Robert Crammer; Wendall Bauman; Gilberto Zamora; Marios S Pattichis; Peter Soliz
Journal:  Invest Ophthalmol Vis Sci       Date:  2011-07-29       Impact factor: 4.799

2.  Comparison of film and digital fundus photographs in eyes of individuals with diabetes mellitus.

Authors:  Sapna Gangaputra; Talat Almukhtar; Adam R Glassman; Lloyd Paul Aiello; Neil Bressler; Susan B Bressler; Ronald P Danis; Matthew D Davis
Journal:  Invest Ophthalmol Vis Sci       Date:  2011-08-03       Impact factor: 4.799

3.  Association of Generalized and Abdominal Obesity with Diabetic Retinopathy in Chinese Type 2 Diabetic Patients.

Authors:  Jiaxian Chen; Yanan Wan; Jian Su; Zheng Zhu; Engchun Pan; Chong Shen; Jinbo Wen; Kai Wang; Hao Yu; Yu Qin; Lan Cui; Jinyi Zhou; Ming Wu
Journal:  Acta Diabetol       Date:  2021-10-28       Impact factor: 4.280

4.  The Beijing Desheng Diabetic Eye Study: rationale, design, methodology and baseline data.

Authors:  Yun-Yun Li; Xiu-Fen Yang; Hong Gu; Xi-Pu Liu; Torkel Snellingen; Ning-Pu Liu
Journal:  Int J Ophthalmol       Date:  2018-01-18       Impact factor: 1.779

5.  C-reactive protein and diabetic retinopathy in Chinese patients with type 2 diabetes mellitus.

Authors:  Xiu-Fen Yang; Yu Deng; Hong Gu; Apiradee Lim; Torkel Snellingen; Xi-Pu Liu; Ning-Li Wang; Amitha Domalpally; Ronald Danis; Ning-Pu Liu
Journal:  Int J Ophthalmol       Date:  2016-01-18       Impact factor: 1.779

6.  The relationship between insulin resistance/β-cell dysfunction and diabetic retinopathy in Chinese patients with type 2 diabetes mellitus: the Desheng Diabetic Eye Study.

Authors:  Yun-Yun Li; Xiu-Fen Yang; Hong Gu; Torkel Snellingen; Xi-Pu Liu; Ning-Pu Liu
Journal:  Int J Ophthalmol       Date:  2018-03-18       Impact factor: 1.779

Review 7.  Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy.

Authors:  Rajiv Raman; Sangeetha Srinivasan; Sunny Virmani; Sobha Sivaprasad; Chetan Rao; Ramachandran Rajalakshmi
Journal:  Eye (Lond)       Date:  2018-11-06       Impact factor: 3.775

Review 8.  Telemedicine for detecting diabetic retinopathy: a systematic review and meta-analysis.

Authors:  Lili Shi; Huiqun Wu; Jiancheng Dong; Kui Jiang; Xiting Lu; Jian Shi
Journal:  Br J Ophthalmol       Date:  2015-01-06       Impact factor: 4.638

Review 9.  Advances in Retinal Imaging and Applications in Diabetic Retinopathy Screening: A Review.

Authors:  Beau J Fenner; Raymond L M Wong; Wai-Ching Lam; Gavin S W Tan; Gemmy C M Cheung
Journal:  Ophthalmol Ther       Date:  2018-11-10

10.  Remote Tool-Based Adjudication for Grading Diabetic Retinopathy.

Authors:  Mike Schaekermann; Naama Hammel; Michael Terry; Tayyeba K Ali; Yun Liu; Brian Basham; Bilson Campana; William Chen; Xiang Ji; Jonathan Krause; Greg S Corrado; Lily Peng; Dale R Webster; Edith Law; Rory Sayres
Journal:  Transl Vis Sci Technol       Date:  2019-12-18       Impact factor: 3.283

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.