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Clinical Ophthalmology Volume 8, 2014 - Issue
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Original Research

Grader agreement, and sensitivity and specificity of digital photography in a community optometry-based diabetic eye screening program

Luckni Sellahewa1 Diabetic Medicine Department, Nottingham University Hospitals, Nottingham, UK;2 North Nottinghamshire Eye Screening Service, Sherwood Forest Hospitals Foundation Trust, University of Nottingham, Nottingham, UK
,
Craig Simpson2 North Nottinghamshire Eye Screening Service, Sherwood Forest Hospitals Foundation Trust, University of Nottingham, Nottingham, UK
,
Prema Maharajan2 North Nottinghamshire Eye Screening Service, Sherwood Forest Hospitals Foundation Trust, University of Nottingham, Nottingham, UK
,
John Duffy2 North Nottinghamshire Eye Screening Service, Sherwood Forest Hospitals Foundation Trust, University of Nottingham, Nottingham, UK
&
Iskandar Idris3 Division of Medical Sciences and Graduate Entry Medicine, School of Medicine, University of Nottingham, Nottingham, UKCorrespondence[email protected]
Pages 1345-1349 | Published online: 17 Jul 2014

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Anas Bilal, Guangmin Sun, Sarah Mazhar, Azhar Imran & Jahanzaib Latif. (2022) A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 10:6, pages 663-674.
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Jorge A Cuadros. (2015) Telemedicine-based diabetic retinopathy screening programs: an evaluation of utility and cost-effectiveness. Smart Homecare Technology and TeleHealth 3, pages 119-127.
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Articles from other publishers (5)

Elias Khalili Pour, Khosro Rezaee, Hossein Azimi, Seyed Mohammad Mirshahvalad, Behzad Jafari, Kaveh Fadakar, Hooshang Faghihi, Ahmad Mirshahi, Fariba Ghassemi, Nazanin Ebrahimiadib, Masoud Mirghorbani, Fatemeh Bazvand, Hamid Riazi-Esfahani & Mohammad Riazi Esfahani. (2022) Automated machine learning–based classification of proliferative and non-proliferative diabetic retinopathy using optical coherence tomography angiography vascular density maps. Graefe's Archive for Clinical and Experimental Ophthalmology 261:2, pages 391-399.
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Usharani Bhimavarapu & Gopi Battineni. (2022) Deep Learning for the Detection and Classification of Diabetic Retinopathy with an Improved Activation Function. Healthcare 11:1, pages 97.
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K. Gunasekaran, R. Pitchai, Gogineni Krishna Chaitanya, D. Selvaraj, S. Annie Sheryl, Hesham S. Almoallim, Sulaiman Ali Alharbi, S. S. Raghavan & Belachew Girma Tesemma. (2022) A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs. BioMed Research International 2022, pages 1-15.
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Amin Valizadeh, Saeid Jafarzadeh Ghoushchi, Ramin Ranjbarzadeh & Yaghoub Pourasad. (2021) Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network. Computational Intelligence and Neuroscience 2021, pages 1-14.
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Gen-Min Lin, Mei-Juan Chen, Chia-Hung Yeh, Yu-Yang Lin, Heng-Yu Kuo, Min-Hui Lin, Ming-Chin Chen, Shinfeng D. Lin, Ying Gao, Anran Ran & Carol Y. Cheung. (2018) Transforming Retinal Photographs to Entropy Images in Deep Learning to Improve Automated Detection for Diabetic Retinopathy. Journal of Ophthalmology 2018, pages 1-6.
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